# 33rd Annual Conference International Society for Clinical

## Comments

## Transcription

33rd Annual Conference International Society for Clinical

Programme & abstract book rd 33 Annual Conference of the International Society for Clinical Biostatistics 19-23 August 2012 – Bergen, Norway 2/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info Welcome to Bergen 2012 We are delighted to invite you to the 33rd Annual Conference of the ISCB, for the first time in Norway. Clinical biostatistics is a field that is crucial to medical and health related research to ensure quality knowledge as basis for treatment and prevention, etiology and health care. The conference is meant to build a bridge between researchers in the medical and health related fields and developers of new relevant statistical methodology and software. The Scientific Programme Committee has set up a broad range of interesting topics both in the invited and contributed session. The traditional mini-symposia on Thursday 23 August, that can be attended separately, is devoted to Register-based epidemiology and Novel statistical approaches used in post-marketing safety surveillance systems, thus giving the conference a Nordic flavour as well as a global perspective. The conference is also a unique opportunity to experience Norwegian nature and culture, be it before, during, or after the official programme. We hope you find the programme attractive and will take this opportunity to participate and visit Bergen, Norway in August 2012. Mark your calendar now! Geir Egil Eide Chair Local Organizing Commitee ISBN: 978-82-8045-026-5 Odd O. Aalen Chair Scientific Programme Committee ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info 3/156 Content Welcome to Bergen 2012.................................................................................................................................................................2 Content.............................................................................................................................................................................................3 International Society for Clinical Biostatistics (ISCB)........................................................................................................................4 Programme Overview.......................................................................................................................................................................6 Scientific Programme........................................................................................................................................................................8 Abstracts – Oral Presentations........................................................................................................................................................29 Sunday, 19 August – Pre Conference Courses.........................................................................................................................29 Monday, 20 August...................................................................................................................................................................31 Morning sessions (IP1, I1 , C1 – C4) ..............................................................................................................................31 Afternoon sessions (IP2, I2 , C5 – C13) ..........................................................................................................................36 Tuesday, 21 August..................................................................................................................................................................48 Morning sessions (I3 - I4 , C14 – C21) ...........................................................................................................................48 Wednesday, 22 August.............................................................................................................................................................59 Morning sessions (IP3, I5 , C22 – C25) ..........................................................................................................................59 Afternoon sessions (I6 , C26 - 34) ...................................................................................................................................64 Thursday, 23 august - Mini-symposia........................................................................................................................................77 Abstracts - Posters..........................................................................................................................................................................80 Author's Index...............................................................................................................................................................................133 Information for Presenters.............................................................................................................................................................146 Statistics in Medicine Special Issue..............................................................................................................................................147 ISCB Awards................................................................................................................................................................................147 Acknowledgements.......................................................................................................................................................................148 Conference Venue........................................................................................................................................................................149 General Information......................................................................................................................................................................150 Vocabulary - Ordbok.....................................................................................................................................................................151 Social Events................................................................................................................................................................................152 Map of Bergen..............................................................................................................................................................................154 Posters and Exhibitors Placement................................................................................................................................................155 Plan Grieghallen...........................................................................................................................................................................156 4/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info International Society for Clinical Biostatistics (ISCB) The International Society for Clinical Biostatistics (ISCB) was founded in 1978 to stimulate research into the principles and methodology used in the design and analysis of clinical research and to increase the relevance of statistical theory to the real world of clinical medicine. Membership is open to all interested individuals who share the Aims of the Society. ISCB’s membership include clinicians, statisticians and members of other disciplines, such as epidemiologists, clinical chemists and clinical pharmacologists, working or interested in the field of clinical biostatistics. President: Harbajan Chadha-Borham (Switzerland) Vice-president: Koos Zwinderman (The Netherlands) Secretary: David W. Warne (Switzerland) Treasurer: KyungMann Kim (USA) Webmaster: Ingrid Sofie Harboe (Denmark) Ordinary members: Michal Abramovicz (Canada) Lucinda Billingham (UK) Krisztina Boder (Hungary) Tomasz Burzykowski (Belgium) Lutz Edler (Germany) Catherine Legrand (Belgium) Saskia Le Sessie (The Netherlands) Giota Toulumi (Greece) Zdeněk Valenta (Czech Republic) Scientific Programme Committee Chair: Odd O. Aalen (Norway) Members: Per Kragh Andersen (Denmark) Jan Beyersmann (Germany) Ørnulf Borgan (Norway) Michael Campbell (UK) David Clayton (UK) Daniel Commenges (France) Vanessa Didelez (UK) Clelia Di Serio (Italy) Jan Terje Kvaløy (Norway) Sophia Rabe-Hesketh (USA) Marie Reilly (Sweden) Terry Therneau (USA) Stein Emil Vollset (Norway) ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info 5/156 Local Organising Committee Chair: Geir Egil Eide (Centre for Clinical Research, Bergen) Vice-chairman: Stein Atle Lie (Uni Health, Bergen) Secretary: Anne Marie Fenstad (Arthroplasty Registry, Bergen) Treasurer: Jan Harald Aarseth (MS Registry, Bergen) Scientific coordinator: Jan Terje Kvaløy (University of Stavanger, Stavanger) Course coordinator: Tore Wentzel-Larsen (RBUP/NKVTS, Oslo) Symposium coordinator: Roy Miodini Nilsen (Centre for Clinical Research, Bergen) Social program coordinator: Ågot Irgens (Dep. of Occupational Medicine, Bergen) Sponsorship acquisition : Stein Atle Lie (Uni Health, Bergen) Milada Cvancarova Småstuen (Cancer Registry, Oslo) Webmaster/ editor: Jörg Aßmus (Centre for Clinical Research, Bergen) Associate member: Ivar Heuch (Dep. of Mathematics, University of Bergen) Mini-symposium Committee Members: Stein Emil Vollset (University of Bergen, Bergen) Rolv A. Skjærven (University of Bergen, Bergen) Tone Bjørge (University of Bergen, Bergen) Marjolein Iversen (Bergen University College, Bergen) Roy Miodini Nilsen (Centre for Clinical Research, Bergen) Congress Secretariat Kongress & Kultur AS Torgalmenningen 1A Postboks 947 Sentrum N-5808 Bergen Phone: + 47 55 55 36 55 Fax: + 47 55 55 36 56 e-mail: [email protected] 6/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info Programme Overview Time Reg. 07:15 07:30 07:45 08:00 08:15 08:30 08:45 09:00 09:15 09:30 09:45 10:00 10:15 10:30 10:45 11:00 11:15 11:30 11:45 12:00 12:15 12:30 12:45 13:00 13:15 13:30 13:45 14:00 14:15 14:30 14:45 15:00 15:15 15:30 15:45 16:00 16:15 16:30 16:45 17:00 17:15 17:30 17:45 18:00 18:30 19:00+ All day Sunday 19 August Registration 8.00-21.00 Registration Pre-conference courses 1, 2, 3 Monday 20 August Registration 7.30-16.30 Tuesday 21 August Registration 7.30-13.00 Registration Registration Welcome to ISCB33 Plenary session: Stephen Senn (IP1) Refreshments Refreshments Pre-conference courses 1, 2, 3 Invited session: Infectious diseases (I1) Contributed sessions (C1-C4) Invited session: Functional data analysis (I3) Contributed sessions (C14-C17) Poster session and refreshments Invited session: Extensions to epidemiological designs (I4) Contributed sessions (C18-C21) Lunch Lunch Pre-conference courses 1, 4, 5 Refreshments Pre-conference courses 1, 4, 5 Lunch Invited session: Evaluating hospital performance (I2) Contributed sessions (C5-C8) Refreshments Plenary session: Terry Speed (IP2) Conference excursions Break Contributed sessions (C9-C13) Registration (till 21:00) 19:00 – 20:30: Welcome reception Poster session Poster session ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info 7/156 Programme Overview Wednesday 22 August Registration 7.30-16.30 Thursday 23 August Registration 8.00-13.00 Registration Registration Invited session: Causal inference (I5) Contributed sessions (C22-C25) Refreshments President's invited Speaker Debora Ashby (IP3) Annual General Meeting (AGM) Lunch Invited session: Genomics and system biology (I6) Mini-Symposium Registerbased epidemiology (MS1) Mini-Symposium Statistics in vaccine research (MS2) Refreshments Mini-Symposium Registerbased epidemiology (MS1) Mini-Symposium Statistics in vaccine research (MS2) Closure of ISCB33 Contributed sessions (C26-C29) Refreshments Contributed sessions (C30-C34) Time Reg. 07:15 07:30 07:45 08:00 08:15 08:30 08:45 09:00 09:15 09:30 09:45 10:00 10:15 10:30 10:45 11:00 11:15 11:30 11:45 12:00 12:15 12:30 12:45 13:00 13:15 13:30 13:45 14:00 14:15 14:30 14:45 15:00 15:15 15:30 15:45 16:00 16:15 16:30 16:45 17:00 17:15 17:30 17:45 18:00 18:30 19:00-23:00 Conference dinner 19:00+ Poster session All day 8/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info Posters Thursday, 23/7 Wednesday, 22/7 Tuesday, 21/7 Monday, 20/7 Sunday, 19/7 Scientific Programme SUNDAY, 19 AUGUST 2012 Pre-conference courses Småtroll 1 09:00-17:30 Michael Norhtnagel and Bettina Kulle Andreassen Analysis of rare genetic variants in common diseases Troldtog 2 09:00-12:45 Thiago Guerra Martins Bayesian computing with INLA Klokkeklang 3 09:00-12:45 Miguel Hernán Estimating treatment effects using longitudinal data Klokkeklang 4 13:45-17:30 Philip Hougaard Analysis of interval-censored survival data Troldtog 5 13:45-17:30 Hélène Jacqmin-Gadda and Cécile Proust-Lima Latent class mixed models for longitudinal data and time-to-event data MONDAY, 20 AUGUST 2012 Peer Gynt 08:30-10:00 IP1 08:30-09:00 09:00-10:00 09:00-10:00 Klokkeklang I1 10:30-12:00 I1.1 10:30-11:00 I1.2 11:00-11:30 I1.3 11:30-12:00 Peer Gynt C1 10:30-12:00 C1.1 10:30-10:48 C1.2 10:48-11:06 Welcome and Opening of the Conference, Opening Plenary Session Geir Egil Eide (LOC Chair) LOC Chair, ISCB President, Director Haukeland University Hospital Plenary session Chair: Odd O. Aalen (SPC Chair) Stephen Senn Concurrent control: key or sacred cow? Invited session: Modeling infectious disease Chair: Birgitte de Blasio Gianpaolo Scalia Tomba Modeling disease spread for insight, hindsight, foresight... Michiel van Boven Estimation of vaccine efficacy in a disease Christopher Fraser Epidemiological and evolutionary dynamics of HIV-1 virulence Contributed session: Causal inference I Chair: Miguel Hernan Vanessa Didelez Covariates and Confounding in Instrumental Variable Analyses Kjetil Røysland 11:42-12:00 Troldtog C2 10:30-12:00 C2.1 10:30-10:48 C2.2 10:48-11:06 C2.3 11:06-11:24 C2.4 11:24-11:42 C2.5 11:42-12:00 Gjendine C3 10:30-12:00 C3.1 10:30-10:48 C3.2 10:48-11:06 C3.3 11:06-11:24 C3.4 11:24-11:42 C3.5 11:42-12:00 Småtroll C4 10:30-12:00 C4.1 10:30-10:48 C4.2 10:48-11:06 Contributed session: Adaptive clinical trials Chair: KyungMann Kim Tim Friede Treatment Selection in Seamless Phase II/III Trials Incorporating Information on Short-term Endpoints Emmanuel Lesaffre Comparative Bayesian escalation designs Karen Pye A Bayesian Approach to Dose-Finding Studies for Cancer Therapies: Incorporating Later Cycles of Therapy J. Jack Lee Bayesian Outcome-Adaptive Randomization in Clinical Trials Adelaide Doussau Phase I dose finding methods using longitudinal data and proportional odds model in oncology Contributed session: Bioinformatics Chair: Thomasz Burzykowski Shu Mei Teo Challenges associated with detecting copy number variants using depth of coverage with nextgeneration sequencing technology. Stefanie Hieke Integration of multiple genome wide data sets in clinical risk prediction models Veronika Rockova Incorporation of Prior Biological Knowledge in Bayesian Variable Selection of Genomic Features Nuala A. Sheehan Participant Identification in Genetic Association Studies: Methods and Practical Implications Bart Van Rompaye Variant detection in D pooled DNA samples Contributed session: Multistate models Chair: Zdenek Valenta Bendix Carstensen Multistate models with multiple time scales Jiri Jarkovsky Risk factors of rehospitalisation and death for acute heart failure using multistate survival models Sunday, 19/7 C1.5 Monday, 20/7 11:24-11:42 Tuesday, 21/7 C1.4 Natural effect propagation Machteld Varewyck A comparison of statistical methods for benchmarking clinical centers in terms of quality of care Saskia le Cessie Comparing population effects of different intervention policies, using a combination of inverse probability weighting and G-computation Marinus J. C. Eijkemans Implementation of G-computation with complex longitudinal data Wednesday, 22/7 11:06-11:24 Thursday, 23/7 C1.3 9/156 Posters ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info 10/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info Sunday, 19/7 I2.1 13:30-14:00 I2.2 14:00-14:30 I2.3 14:30-15:00 Tuesday, 21/7 C4.4 11:24-11:42 Monday, 20/7 C4.3 11:06-11:24 C4.5 11:42-12:00 Klokkeklang I2 13:30-15:00 Troldtog C5 13:30-15:00 C5.1 13:30-13:48 Thursday, 23/7 Wednesday, 22/7 C5.2 13:48-14:06 C5.3 14:06-14:24 C5.4 14:24-14:42 C5.5 14:42-15:00 Gjendine C6 13:30-15:00 C6.1 13:30-13:48 Posters C6.2 13:48-14:06 C6.3 14:06-14:24 Biswabrata Pradhan Semi-parametric Estimation of Quality Adjusted Lifetime Distribution in Semi-Markov Illness-Death Models Micha Mandel Estimating time to disease progression comparing transition models and survival methods Liesbeth C. de Wreede Modelling Graft-versus-Host-Disease: statistical approaches incorporating clinical aspects Invited session: Evaluating hospital performance Chair: Michael J. Campbell Michael J. Campbell Developing a summary hospital mortality index: how can we compare hospitals? A retrospective analysis of all English hospitals over 5 years Hayley Jones Some statistical issues in identifying 'unusual' healthcare providers: multiple testing, regression-tothe-mean and outliers versus extremes Alex Bottle Traditional and machine learning methods for comorbidity adjustment in mortality risk models Contributed session: Clinical trials I Chair: Nicole Close Emmanuel Aris Linear Categorical Marginal Modeling of Solicited Symptoms in Vaccine Clinical Trials Elizabeth Williamson Variance estimation for propensity scores in randomised trials Elasma Milanzi Properties of Estimators in Exponential Family Settings With Observation-based Stopping RulesProperties of Estimators in Exponential Family Settings With Observation-based Stopping Rules Suzanne Lloyd Use of record linkage to conduct long-term follow-up of a clinical trial and to investigate generalising cohorts to the underlying population Chris Metcalfe Estimating the optimal treatment effect when the randomised controlled trial design incorporates variable exposure to active intervention Contributed session: Epidemiological designs Chair: Ørnulf Borgan Eiliv Lund Methodological challenges by the globolomic design - the Norwegian Women and Cancer postgenome cohort Anna Johansson Analysis of case-cohort studies using flexible parametric models Aksel Jensen The case-time-control design with multiple reference periods C7.1 13:30-13:48 C7.2 13:48-14:06 C7.3 14:06-14:24 C7.4 14:24-14:42 C7.5 14:42-15:00 Småtroll C8 13:30-15:00 C8.1 13:30-13:48 C8.2 13:48-14:06 C8.3 14:06-14:24 C8.4 14:24-14:42 C8.5 14:42-15:00 Peer Gynt IP2 15:30-16:30 15:30-16:30 Contributed Session: Survival analysis I Chair: Maja Pohar Perme Michal Abrahamowicz New Method for Controlling for Unobserved Confounding in Time to Event Analyses of Comparative Effectiveness and Safety of Drugs Jennifer Rogers Analysis of repeat event outcome data in clinical trials: examples in heart failure Katy Trébern-Launay A multiplicative-regression model to compare the effect of factors associated with the time to graft failure between first and second renal transplant Peggy Sekula Risk assessment of time-varying factors on an acute event using the case-crossover method: A simulation study Khangelani Zuma Analysis of complex correlated interval-censored HIV data from population based survey Contributed session: Modeling infectious disease Chair: Michael Nothnagel Steffen Unkel Time-varying frailty models and the estimation of heterogeneities in transmission of infectious diseases Prague Mélanie Toward information synthesis with mechanistic models of HIV dynamics Cédric Laouénan Modeling hepatitis C viral kinetics to compare antiviral potencies of two protease inhibitors: a simulation study under real conditions of use Paddy Farrington Estimation of the basic reproduction number for infectious diseases with age-varying individual heterogeneity in contact rates Vana Sypsa A mathematical model used as a tool to estimate carbapenemase-producing Klebsiella pneumoniae transmissibility and to assess the impact of potential interventions in the hospital setting Plenary session Chair: Ivar Heuch Terry Speed Epigenetics: A new frontier Sunday, 19/7 Peer Gynt C7 13:30-15:00 Monday, 20/7 14:42-15:00 Tuesday, 21/7 C6.5 Sven Ove Samuelsen Inverse probability weighting for nested case-control studies: Application to a study of vitamin-D and prostate cancer. Nathalie C. Støer Inverse probability weighting for nested case-control studies: A simulation study related to a nested case-control study of vitamin-D and prostate cancer. Wednesday, 22/7 14:24-14:42 Thursday, 23/7 C6.4 11/156 Posters ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info 12/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info Sunday, 19/7 Klokkeklang C9 16:45-18:00 C9.1 16:45-17:03 C9.2 17:03-17:21 C9.3 17:21-17:39 Peer Gynt C10 16:45-18:00 Tuesday, 21/7 C10.2 17:03-17:21 Wednesday, 22/7 C10.1 16:45-17:03 Troldtog C11 16:45-18:00 Thursday, 23/7 Monday, 20/7 C9.4 17:39-17:57 C11.4 17:39-17:57 C10.3 17:21-17:39 C10.4 17:39-17:57 C11.1 16:45-17:03 C11.2 17:03-17:21 C11.3 17:21-17:39 Gjendine C12 16:45-18:00 Posters C12.1 16:45-17:03 C12.2 17:03-17:21 Contributed session: Causal inference II Chair: Vanessa Didelez Stijn Vansteelandt Simple estimation strategies for natural direct and indirect effects Jack Bowden Effective use of RPSFTM's in late stage cancer trials with substantial treatment cross-over Jenny Häggström Targeted Smoothing Parameter Selection for Estimating Average Causal Effects Jozefin Buyyze Estimating random center effects using instrumental variables Contributed Session: Meta-analyses Chair: Willi Sauerbrei Gerta Rücker Graph theory meets network meta-analysis Ralf Bender Impact of Network Size and Inconsistency on the Results of MTC Meta-Analyses Ulrike Krahn Model Selection for Locating of Incoherence in Network Meta-Analysis Jochem König Adapting Cochran's Q for Network Meta-Analysis Contributed session: Evaluating hospital performance Chair: Alex Bottle Jessica Kasza Evaluation and comparison of the performance of Australian and New Zealand intensive care units Shalini Santhakumaran Evaluating mortality rates for neonatal units using multiple membership models Erik van Zwet Confidence intervals for ranks with application to performance indicators Yew Yoong Ding Assessing Hospital Performance for Pneumonia Using Administrative Data With and Without Clinical Data: Does the Difference Matter? Contributed session: Latent variable models Chair: Hélène Jacqmin-Gadda Youngjo Lee Extended likelihood approach to large-scale multiple testing Peter Congdon Interpolation between spatial frameworks: an application of process convolution to estimating neighbourhood disease prevalence C13.1 16:45-17:03 C13.2 17:03-17:21 C13.3 17:21-17:39 C13.4 17:39-17:57 Contributed session: Functional data analysis/longitudinal data Chair: Emannuel Lesaffre Daniela Adolf Parametric and non-parametric multivariate analysis of functional MRI data Kathrine Frey Frøslie Path analysis with multilevel functional data: Change in glucose curves during pregnancy and its impact on birth weight. Stanislav Katina Automatic identification and analysis of anatomical curves across human face Susan Bryan Prediction of Visual Prognosis to Optimize Frequency of Perimetric Testing in Glaucoma TUESDAY, 21 AUGUST 2012 Klokkeklang I3 08:30-10:00 I3.1 08:30-09:00 I3.2 09:00-09:30 I3.3 09:30-10:00 Peer Gynt C14 08:30-10:00 C14.1 08:30-08:48 C14.2 08:48-09:06 C14.3 09:06-09:24 C14.4 09:24-09:42 Invited session: Functional data analysis Chair: Per Kragh Andersen Helle Søresen Functional data – an introduction towards applications Jeff Goldsmith A Modular Framework for Scalar-on-Function Regression Laura Sangalli A study of cerebral aneurysms pathogenesis: functional data analysis of three-dimensional geometries of the inner carotid artery Contributed session: Clinical trials II Chair: Elizabeth Williamson Gerard van Breukelen Efficient design of cluster randomized trials with treatment-dependent sampling costs and treatment-dependent unknown outcome variances Daniel Lorand Bayesian Phase II randomized design for time-to-event endpoint using historical control Application to Oncology Andrew Forbes Evaluation of methods for design and analysis of cluster randomised crossover trials with binary outcomes with application to intensive care research Atanu Biswas Optimal target allocation proportion for correlated binaryresponses in a two-treatment clinical trial Sunday, 19/7 Småtroll C13 16:45-18:00 Monday, 20/7 17:39-17:57 Tuesday, 21/7 C12.4 Sophie Ancelet Bayesian shared spatial-component models to combine sparse and heterogeneous epidemiological data informing about a rare disease and detect spatial biases. Danielle Belgrave An Investigation of Latent Class Trajectory Models of Prescribing to Define a Phenotypic Marker of Disease Susceptibility Wednesday, 22/7 17:21-17:39 Thursday, 23/7 C12.3 13/156 Posters ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info 14/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info Sunday, 19/7 C14.5 09:42-10:00 Gjendine C15 08:30-10:00 C15.1 08:30-08:48 Thursday, 23/7 Wednesday, 22/7 Tuesday, 21/7 Monday, 20/7 C15.2 08:48-09:06 C15.3 09:06-09:24 C15.4 09:24-09:42 C15.5 09:42-10:00 Troldtog C16 08:30-10:00 C16.1 08:30-08:48 C16.2 08:48-09:06 C16.3 09:06-09:24 C16.4 09:24-09:42 C16.5 09:42-10:00 Småtroll C17 08:30-10:00 C17.1 08:30-08:48 Posters C17.2 08:48-09:06 C17.3 09:06-09:24 C17.4 09:24-09:42 Nicole Close Statisticians Implementing Change and Cost Effectiveness in Clinical Trials through Risk Prioritization Monitoring Based Contributed session: Statistics for epidemiology I Chair: Paddy Farrington Nadine Binder Bias of relative risk estimates in cohort studies as induced by missing information due to death Michael Schemper Explained variation versus attributable risk Luwis Diya Quantifying bias in register based research Michael Johnson Mahande Recurrence risk of perinatal mortality in Northern Tanzania: A registry-based prospective cohort study Oliver Collingnon Luxemburg acUte myoCardial Infarction registry (LUCKY): estimation of the effect of clinical and biochemical variables on the New-York Heart Association score using penalized ordinal logistic regression. Contributed session: Joint modeling of outcome and time-to-event Chair: Clelia Di Serio Magdalena Murawska (Student award winner) Dynamic Prediction Based on Joint Model for Categorical Response and Time-to-Event Michael Crowther (Student award winner) Adjusting for measurement error in baseline prognostic biomarkers: A joint modelling approach Cécile Proust-Lima Dynamic predictions from joint models for longitudinal biomarker trajectory and time to clinical event: development and validation Jessica Barrett A closed form likelihood for joint modelling of repeated measurements and survival outcomes, with an application to cystic fibrosis data. Ralitza Gueorguieva Joint Modeling of Repeatedly Measured Continuous Outcome and Interval-censored Competing Risk Data Contributed session: Genomics / systems biology Chair: Koos Zwinderman Thomasz Burzykowski High resolution QTL-mapping with whole-genome sequencing data Boris Hejblum Application of Gene Set Analysis of Time-Course gene expression in a HIV vaccine trial Setia Pramana Detecting genetic differences between monozygous twins by next-generation sequencing Mikel Esnaola Modeling count data in RNA-seq experiments using the Poisson-Tweedie family of distributions ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info 11:00-11:30 I4.2 11:30-12:00 I4.3 12:00-12:30 Peer Gynt C18 11:00-12:30 C18.1 11:00-11:18 C18.2 11:18-11:36 C18.3 11:36-11:54 C18.4 11:54-12:12 C18.5 12:12-12:30 Peer Gynt C19 11:00-12:30 C19.1 11:00-11:18 C19.2 11:18-11:36 C19.3 11:36-11:54 C19.4 11:54-12:12 C19.5 12:12-12:30 Bryan Langholz Conditional likelihoods for case-cohort data: Do they exist? Paola Rebora Estimating cumulative incidence adjusting for competing risk using an optimal two-phase stratified design Agus Salim A semiparametric approach to secondary analysis of nested case-control data Contributed session: Clinical trials III Chair: Lucinda Billingham Lehana Thabane Dealing with Criticisms and Controversies of Pragmatic Trials Jeremy Taylor Finding and validating subgroups of enhanced treatment effect in randomized clinical trials David Oakes Monitoring a Long-Term Efficacy Study for Futility: an Application in Huntington's Disease Olympia Papachristofi Assessment of surgical interventions through clinical trials: accounting for the impact of learning curves. Oke Gerke Interim analyses in diagnostic versus treatment studies: differences and similarities Contributed session: Model selection I Chair: Mette Langaas Yunzhi Lin (Student award winner) Advanced Colorectal Neoplasia Risk Stratification by Penalized Logistic Regression Daniel Commenges A universal cross-validation criterion and its asymptotic distribution Carolin Jenker Modeling continuous predictors with a ‘spike’ at zero: multivariable extensions and handling of related spike variables Willi Sauerbrei Stability investigations of multivariable regression models derived from low and high dimensional data Yanzhong Wang Learning Mixtures through merging components Sunday, 19/7 Monday, 20/7 I4.1 Invited session: Extensions to Epidemiological Designs Chair: Marie Reilly Tuesday, 21/7 Klokkeklang I4 11:00-12:30 Poster session All poster presenters Wednesday, 22/7 P1-P22 10:00-11:00 Knut Wittkowski From single-SNP to wide-locus GWAS: A Computational Biostatistics Approach Identifies Pathways in Small Sample Studies Thursday, 23/7 09:42-10:00 Posters C17.5 15/156 16/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info Sunday, 19/7 Troldtog C20 11:00-12:30 C20.1 11:00-11:18 C20.2 11:18-11:36 C20.3 11:36-11:54 Monday, 20/7 C21.1 11:00-11:18 Posters Thursday, 23/7 Wednesday, 22/7 C20.5 12:12-12:30 Tuesday, 21/7 C20.4 11:54-12:12 Småtroll C21 11:00-12:30 C21.2 11:18-11:36 C21.3 11:36-11:54 C21.4 11:54-12:12 C21.5 12:12-12:30 Contributed session: Prediction in survival analysis Chair: Philip Hougaard Heiko Götte Sample size planning for survival prediction with focus on high dimensional data Robin Van Oirbeek Exploring the discriminatory ability of frailty models Audrey Mauguen Prediction tool for risk of death using history of cancer recurrences in joint frailty models Marcel Wolbers Concordance for prognostic models with competing risks Paul Blanche Comparing areas under time-dependent ROC curves under competing risk Contributed session: Statistical methodology I Chair: Jan Terje Kvaløy Yuanzhang Li High dimensional regression using decomposition-gradient-nuisance method and its application in epidemiological case control studies Stian Lydersen Choice of the Berger and Boos confidence coefficient in an unconditional test for equality of two binomial probabilities Christoph Schürmann Surrogate endpoints in breast and colon cancer: An evaluation of validation studies Buddhananda Banerjee Use of surrogate endpoints for improving efficiency, reduction of sample size and modification of Mantel-Haenszel estimator for odds ratio Lyle Gurrin Estimation of between- and within-pair regression effects in logistic regression with shared measurement error WEDNESDAY, 22 AUGUST 2012 Peer Gynt I5 08:30-10:00 I5.1 08:30-09:00 I5.2 09:00-09:30 I5.3 09:30-10:00 Klokkeklang C22 08:30-10:00 Invited session: Causal inference Chair: Stijn Vansteelandt Tyler VanderWeele Causal mediation analysis with applications to perinatal epidemiology Andrea Rotnitzky Estimation and extrapolation of treatment effects Els Goetghebeur Protecting against errors: causal effect estimates for the evaluation of quality of care over many (cancer) centers Contributed session: Survival analysis II Chair: Sven Ove Samuelsen 09:06-09:24 C22.4 09:24-09:42 C22.5 09:42-10:00 Småtroll C23 08:30-10:00 C23.1 08:30-08:48 C23.2 08:48-09:06 C23.3 09:06-09:24 C23.4 09:24-09:42 C23.5 09:42-10:00 Gjendine C24 08:30-10:00 C24.1 08:30-08:48 C24.2 08:48-09:06 C24.3 09:06-09:24 C24.4 09:24-09:42 C24.5 09:42-10:00 Troldtog C25 08:30-10:00 C25.1 08:30-08:48 C25.2 08:48-09:06 Contributed session: Measurement error Chair: Saskia le Cessie Péter Vargha (Scientist award winner) Regression toward the mean and ANCOVA in observational studies Timothy Mutsvari A multilevel misclassification model for spatially correlated binary data: An application in oral health research Kristoffer Herland Hellton Projecting error: Understanding measurement error in principal components Øystein Sørensen Variable Selection by Lasso in Regression with Measurement Error Nicholas de Klerk Adjustment for genotyping measurement error in a case-control study Contributed session: Prediction Chair: Ulrich Mansmann Marine Lorent Relative ROC curves: a novel approach for evaluating the accuracy of a marker to predict the cause-specific mortality Thomas Debray A framework for developing and implementing clinical prediction models across multiple studies with binary outcomes Daniel Stahl Using Machine learning methods for event related potential (ERP) brain activity analysis Khadijeh Taiyari How much data are required to develop a reliable risk model? Z. J. Musoro Dynamic Predictions of Repeated Events of Different Types by Landmarking Sunday, 19/7 C22.3 Monday, 20/7 08:48-09:06 Tuesday, 21/7 C22.2 Maja Pohar Perme Properties of net survival estimation Therese Andersson Estimating the loss in expectation of life due to cancer using flexible parametric survival models Katharina Ingel Sample Size Calculation and Re-estimation for Recurrent Event Data Terry Therneau Mixed Effects Cox Models and the Laplace Transform Janez Stare On using simulations to study explained variation in survival analysis Wednesday, 22/7 08:30-08:48 Contributed session: Multiple imputation methods Chair: Stian Lydersen Shaun Seaman Multiple Imputation of Missing Covariates with Non-Linear Effects and Interactions: an Evaluation of Statistical Methods Oya Kalaycioglu Comparison of multiple imputation methods for repeated measurements studies Posters C22.1 17/156 Thursday, 23/7 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info 18/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info C25.4 09:24-09:42 C25.5 09:42-10:00 Peer Gynt IP3 10:30-11:30 Posters Thursday, 23/7 Wednesday, 22/7 Tuesday, 21/7 Monday, 20/7 Sunday, 19/7 C25.3 09:06-09:24 10:30-11:30 11:30-13:30 Klokkeklang I6 13:30-15:00 I6.1 13:30-14:00 I6.2 14:00-14:30 I6.3 14:30-15:00 Peer Gynt C26 13:30-15:00 C26.1 13:30-13:48 C26.2 13:48-14:06 C26.3 14:06-14:24 C26.4 14:24-14:42 C26.5 14:42-15:00 Troldtog C27 13:30-15:00 C27.1 13:30-13:48 C27.2 13:48-14:06 Georg Heinze Confidence intervals after multiple imputation: combining profile likelihood information from logistic regressions Jonathan Bartlett Congenial multiple imputation of partially observed covariates within the full conditional specification framework John Carlin Diagnosing the goodness-of-fit of models used for multiple imputation President's invited speaker Chair: Harbajan Chadha-Boreham Deborah Ashby A Benefit–Risk Analysis of using Formal Benefit-Risk Approaches for Decision-Making in Drug Regulation Chair: Harbajan Chadha-Boreham Annual General Meeting Invited session: Genomics and systems biology Chair: Arnoldo Frigessi Katja Ickstadt Nonparametric Bayesian Modelling in Systems Biology Linn Cecilie Bergersen Reliable Preselection of Variables in High-dimensional Penalized Regression Problems by Freezing Doug Speed Using Heritability Analysis to Devise a Prediction Model for Epilepsy Contributed session: Comepting risk Chair: Terry Therneau Per Kragh Andersen Decomposing number of life years lost according to causes of death Paul Lambert Parametric modelling of the cumulative incidence function in competing risks models Martin Wolkewitz Nested case-controls studies in cohorts with competing events Giorgios Bakoyannis Late entry bias in cohort studies with competing endpoints Ronald Geskus Inverse probability weighted estimators in survival analysis Contributed session: Statistics for epidemiology II Chair: Stein Emil Vollset Marie Reilly Modeling changes in cancer risk with time from diagnosis of a family member Myeongjee Lee A comprehensive model for jointly estimating familial risk in all first-degree relatives 14:42-15:00 Gjendine C28 13:30-15:00 C28.1 13:30-13:48 C28.2 13:48-14:06 C28.3 14:06-14:24 C28.4 14:24-14:42 C28.5 14:42-15:00 Småtroll C29 13:30-15:00 C29.1 13:30-13:48 C29.2 13:48-14:06 C29.3 14:06-14:24 C29.4 14:24-14:42 C29.5 14:42-15:00 Klokkeklang C30 15:30-17:00 C30.1 15:30-15:48 C30.2 15:48-16:06 C30.3 16:06-16:24 Contributed session: Model selction II Chair: Daniel Commenges Jan Kalina (Scientist award winner) Robust Gene Selection Based on Minimal Shrinkage Redundancy Mar Rodriguez-Girondo Boosting for variable selection in structured survival models Bobrowski Leon Feature subset selection linked to linear separabilty Axel Benner Predictive genomic signatures: Biomarker discovery in high-dimensional data Rosa Meijer A multiple testing method for ordered data Contributed session: Statistical design and methodology I Chair: Hayley Jones Carla Moreira Goodness-of-fit tests for a semiparametric model under a random double truncation Edmund Njeru Njagi A Framework for Characterizing Missingness at Random in Generalized Shared-parameter Joint modeling Framework for Longitudinal and Time-to-Event Data Ikuko Funatogawa Likelihood based estimation for an effect of a time-varying covariate Bruce Tabor Cost-Sensitive Maximum Likelihood Classification: Finding Optimal Biomarker Combinations in Screening and Diagnosis Toby Prevost Designing a preliminary adaptive study to develop biomarker combinations for trial Contributed session: Statistical design and methodology II Chair: John Carlin Stephen Senn Predicting Patient Recruitment in Multi-Centre Clinical Trials Hanhua Liu Evaluation and validation of social and psychological markers: identification and assumptions for instrumental variables estimation Esther de Hoop Sample size calculation for cluster randomized stepped wedge designs Sunday, 19/7 C27.5 Monday, 20/7 14:24-14:42 Tuesday, 21/7 C27.4 Josué Almansa Multinomial multi-latent-class model. Application to multiple exposures in occupational setting and the risk of several histological subtypes of lung cancer. Katherine Lee Modelling the age-dependence of risk in a self-controlled case series analysis Albert Sanchez-Niubo A parametric approach to the reporting delay adjustment method applied to drug use data Wednesday, 22/7 14:06-14:24 Thursday, 23/7 C27.3 19/156 Posters ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info 20/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info Thursday, 23/7 Wednesday, 22/7 Tuesday, 21/7 Monday, 20/7 Sunday, 19/7 C30.4 16:24-16:42 C30.5 16:42-17:00 Peer Gynt C31 15:30-17:00 C31.1 15:30-15:48 C31.2 15:48-16:06 C31.3 16:06-16:24 C31.4 16:24-16:42 C31.5 16:42-17:00 Gjendine C32 15:30-17:00 C32.1 15:30-15:48 C32.2 15:48-16:06 C32.3 16:06-16:24 C32.4 16:24-16:42 C32.5 16:42-17:00 Småtroll C33 15:30-17:00 C33.1 15:30-15:48 C33.2 15:48-16:06 Posters C33.3 16:06-16:24 C33.4 16:24-16:42 C33.5 16:42-17:00 Are Hugo Pripp Lifestyle, socioeconomic factors and consumption of dairy foods analysed with structural equation modeling Luwis Diya Bayesian multilevel factor analytic model for assessing the relationship between nurse-reported adverse events and patient safety Contributed session: Longitudinal data Chair: Cécile Proust-Lima Karolina Sikorska Fast linear mixed model computations for GWAS with longitudinal data Sten Willemsen A Bayesian Model for Multivariate Human Growth Data Riccardo Marioni Cognitive lifestyle and cognitive decline: the characteristics of two longitudinal models Sandra Plancade A statistical model to explore carcinogenic processes by transcriptomics in prospective studies Andrew Copas Analysis of Change Over Time When Measurements are Obtained Only After an Unknown Delay Contributed session: Survival analysis III Chair: Michal Abrahamowicz Anika Buchholz High-dimensional survival studies - comparison of approaches to assess time-varying effects Morten Valberg Frailty modeling of age-incidence curves of osteosarcoma and Ewing sarcoma among individuals younger than 40 years Audrey Mauguen Multivariate frailty models for two types of recurrent events with a dependent terminal event: Application to breast cancer data Donghwan Lee Sparse partial least-squares regression for high-throughput survival data analysis Arthur Allignol A Regression Model for the Extra Length of Stay Associated with a Nosocomial Infection Contributed session: Statistical methodology II Chair: Michael Semper Kerry Leask (Scientist award winner) Modelling Overdispersion in Wadley's Problem with a Beta-Poisson Distribution Ulrich Mansmann Global testing for complex ordinal data Eva Andersson On-line surveillance of air pollution Angela Noufaily An Improved Algorithm for Outbreak Detection in Multiple Surveillance Systems Siegfried Kropf The use of symmetric and asymmetric distance measures for high-dimensional tests of inferiority, equivalence and non-inferiority ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info 15:30-15:48 C34.2 15:48-16:06 C34.3 16:06-16:24 C34.4 16:24-16:42 C34.5 16:42-17:00 Miriam Gjerdevik Improving the error rates of the Begg and Mazumdar test for publication bias in meta-analysis Anissa Elfakir Dealing with missing binary outcome data in meta-analysis: application to randomized clinical trials in nutrition Antonio Gasparrini Multivariate meta-analysis for non-linear and other multi-parameter associations Cono Ariti Developing a predictive risk model for mortality from multiple cohort studies: an example in heart failure Brunilda Balliu Combining Family and Twin Data in Association Studies to Estimate the Non-inherited Maternal Antigens Effect Sunday, 19/7 C34.1 Contributed session: Meta-analyses/Combined data sources Chair: Sada Nand Dwivedi Monday, 20/7 Troldtog C34 15:30-17:00 21/156 09:00-09:30 MS1.2 09:30-10:00 MS1.3 09:06-09:24 MS1.4 10:30-11:00 11:00-11:30 MS1.5 11:30-12:00 MS1.6 12:00-12:30 MS1.7 12:30-13:00 Rolv Skjærven A woman’s reproductive history is related to diseases later in life Timo Hauklinen What can be achieved with a good population-based cancer registry? Giske Ursin How can cancer registries improve our biological understanding of cancer and cancer care? Refreshments Nancy L. Pedersen Double delights through twin registry research Kaare Christensen Register-based research on the epidemiology of aging Sven Cnattingius The Birth Register – how do we find the most beautiful flowers in the garden? Allen J. Wilcox Heterogeneity of risk and selective fertility – Subtle biases produce serious confusions Mini-symposium on Statistics in Vaccines Research Topic: Novel Statistical Approaches Used in Post-marketing Safety Surveillance Systems – A Global Perspective Organiser: Jennifer Nelson and Allen Izu Klokkeklang MS2 09:00-10:30 Mini-symposium on Statistics in Vaccines Research Chair: Allen Izu MS2.1 09:00-09:30 Michael Nguyen FDA’s Sentinel Initiative: Active Vaccine Safety Surveillance and Pharmacovigilance Wednesday, 22/7 MS1.1 Mini-symposium on Registerbased Epidemiology Chair: Stein Atle Lie Thursday, 23/7 Peer Gynt MS1 09:00-13:00 Posters Mini-symposium on Registerbased Epidemiology Organiser: Roy Miodini Nilsen Tuesday, 21/7 THURSDAY, 23 AUGUST 2012 Tuesday, 21/7 Monday, 20/7 Sunday, 19/7 22/156 MS2.2 09:30-10:00 MS2.3 10:00-10:30 MS2.4 10:30-11:00 11:00-13:00 11:00-11:30 MS2.5 11:30-12:00 MS2.6 12:00-12:30 12:30-13:00 Jennifer C. Nelson Methodological challenges for sequential vaccine safety surveillance using observational health care data Lingling Li Drug and Vaccine Safety Surveillance: some existing methods and the unmet analytic needs Refreshments Chair: Jennifer Nelson Stanley Xu Signal Detection of Adverse Events Using Electronic Data with Outcome Misclassification Paddy Farrington Paediatric vaccine pharmacoepidemiology : classification bias in case series analysis and application to febrile convulsions Yonas G. Weldeselassie Self controlled case series method with smooth age effect Discussion Posters P1 P1.1 P1.2 P1.3 P1.4 Wednesday, 22/7 P1.6 P1.7 Thursday, 23/7 P1.5 P2.2 Posters ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info P1.8 P1.9 P2 P2.1 P3 P3.1 P3.2 P3.3 P3.4 P3.5 P3.6 P3.7 P3.8 P3.9 Adaptive clinical trials Wenle Zhao: Response Adaptive Randomization - Cost, Benefit and Implementation with Covariate Balancing Alexandra Graf: Maximum type 1 error rate inflation in multi-armed clinical trials with interim sample size modifications. Yunchan Chi: Adaptive two-stage designs for comparing two binomial proportions in phase II clinical trials Babak Choodari-Oskooei: Impact of lack-of-benefit stopping rules on treatment effect estimates of two-arm multistage (TAMS) trials with time to event outcome Ruud Boessen: Optimizing trial design in pharmacogenetics research; comparing a fixed parallel group, group sequential and adaptive selection design on sample size requirements. Emma McCallum: In a two-stage dose-finding study, how big should the first stage be? Graham Wheeler: Incorporating prior information into dual-agent Phase I dose-escalation studies from single-agent trials Simon Schneider: Blinded and unblinded internal pilot study designs for clinical trials with overdispersed count data Eunsik Park: Group sequential testing in covariate-adjusted response-adaptive designs Bioinformatics Chen Suo: Joint estimation of isoform expression and isoform-specific read distribution using RNA-Seq data across samples Woojoo Lee: Unequal group covariances in microarray data analyses Causal inference Kimberley Goldsmith: Exploration of instrumental variable methods for estimation of causal mediation effects in the PACE trial of complex treatments for chronic fatigue syndrome Richard Emsley: Principal trajectories: extending principal stratification for repeated measures Toby Prevost: Improving the detection of causal mediation effects in complex intervention trials Michel Hof: Estimating the effect of insulin treatment in diabetic type-II patients on cardiovascular disease rates with marginal structural models Sabine Landau: Causal inference from trials of complex interventions Silvana Romio: Marginal Structural Models in Epidemiology: Why not? Eléonore Herquelot: Average treatment effect estimation with a rare binary outcome: an example and simulations Roseanne McNamee: G-estimation from an RCT comparing 2 active treatments and placebo given postrandomisation crossover and simultaneous treatments Georgia Vourli: Direct and indirect effects in the presence of time-dependent confounding P4.6 P4.7 P4.8 P4.9 P4.10 P4.11 P4.12 P4.13 P4.14 P4.15 P4.16 P4.17 P4.18 P4.19 P4.20 P4.21 P4.22 P4.23 P4.24 P4.25 P4.26 P4.27 P4.28 P4.29 P4.30 P5 P5.1 Consulting Lehana Thabane: 10 tips for enhancing biostatistical consultations or collaborations in clinical research: lessons from the trenches Sunday, 19/7 Monday, 20/7 P4.4 P4.5 Clinical trials Jen-pei Liu: Application of the Parallel Line Assay to Assessment of Biosimilar Drug Products Christoph Gerlinger: Statistical derivation of a responder definition for the reduction of hot flushes Stephen D Walter: Optimisation of the two-stage randomised trial design when some participants have no preferred treatment. Yuko Palesch: Revisiting baseline covariate adjustment in randomization and analysis of large clinical trials Wilhelm Gaus: Is a Controlled Randomised Trial the Non-plus-ultra Design? An Advocacy for Comparative, Controlled, Non-randomised Trials Natalja Strelkowa: A biomarker-based designs for a controlled phase II trial in oncology. Mathai A.K.: Regression model to analyze the continuous primary end point in RCTs when the treatment effect depends on baseline values of the outcome variable Thomas Jaki: Confidence intervals for the ratio of AUCs in cross-over bioequivalence trials Willi Sauerbrei: Interaction of treatment with a continuous variable: simulation study of significance level for several methods of analysis John-Philip Lawo: Comparison of groups in the presence of bimodality Brennan C Kahan: Analysis of multicentre trials with continuous or binary outcomes Suzie Cro: Are Appropriate Outcome Measures Being Used in Open-label Randomised Trials? Math Candel: Sample size corrections for varying cluster sizes when testing treatment effects in two-armed randomized trials with heterogeneous clustering Hong Sun: Within-center imbalance after balanced allocation using minimization method: Center as a stratification factor in multi-center clinical trials? Gang Li: A Semiparametric Accelerated Failure Time Mixture Model for Latent Subgroup Analysis of a Randomized Clinical Trial Katrin Roth: Analysis of recurrent event data - an applied comparison of methods using clinical data Primrose Beryl Gladstone: Probability of Inferiority in Current Non-Inferiority Trials Rachid el Galta: Blinded estimation of within subject variance Naohiro Yonemoto: Analysis of case scenario cross-over trial: an application of medical devices manikin study Gloria Crispino O’Connel: Investigating the Strength of the Association between the Amplitude of the Impedance Cardiogram (ICG), Thrust and Depth during CPR compressions. Emmanuel Bouillaud: New approaches for design and analysis of pediatric pharmacokinetic and pharmacokinetic/pharmacodynamic studies Alberto Morabito: Propensity score and area under a ROC curves in repeated measures clinical studies Lisa Belin: Optimization of managing the lost to follow up patients in a Phase II oncology trials. Robert Parker: Blocking in Unblinded Randomized Clinical Trials Karen Smith: Non-Inferiority Trials of Non-Pharmaceutical Interventions Jacob Agris: How to Select Readers for Clinical Trials When There is No Gold Standard Wenle Zhao: Cost and Prevention Strategies of Randomization Errors in Emergency Treatment Clinical Trials Andrew Forbes: Short interrupted time series designs in clinical practice and policy research: an analysis approach using restricted maximum likelihood Gang Li: A Bayesian approach to assess the active control treatment effect for the design of non-inferiority trials Ly-Mee Yu: Sample size calculation for time-to-event outcomes in randomized controlled trials: A review of published trials Tuesday, 21/7 P4 P4.1 P4.2 P4.3 Wednesday, 22/7 P3.12 Unfortunately, this poster has been withdrawn. Fabiola Del Greco M.: Investigation of pleiotropy in Mendelian randomisation studies that use aggregate genetic data Emmanuel Caruana: A new performance measure of propensity score model Thursday, 23/7 P3.10 P3.11 23/156 Posters ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info 24/156 Posters Thursday, 23/7 Wednesday, 22/7 Tuesday, 21/7 Monday, 20/7 Sunday, 19/7 P6 P6.1 P6.2 P6.3 P6.4 P7 P7.1 P7.2 P7.3 P7.4 P7.5 P8 P8.1 P8.2 P8.3 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info Diagnostic methodology Jose Antonio Roldán Nofuentes: Average kappa coefficient: a new measure of accuracy of a binary diagnostic test Antoine Regnault: Applying Partial Least Squares Discriminant Analysis (PLS-DA) for optimisation of decision rules based on complex patient-reported data: creation of the FibroDetect® scoring method Harbajan Chadha-Boreham: Refined nomograms to enhance the interpretation of clinical risk prediction models Jean- Christophe Thalabard: Comparative assessement of a new imaging technique versus an imperfect invasive gold standard : early detection of coronary stenosis after arterial switch surgery in children Epidemiological designs Mohammad Reza Maracy: Cancer incidence and prevalence: application of mortality data to estimates and projects for the period 2001-2015, Iran Ralph Rippe: Selection bias in obesity research: when do sampling weights solve the problem? Sarah Barry: The Parenting Support Framework in Glasgow: mapping variability in behavioural difficulties Riccardo Pertile: Pesticides exposure in an apples growing valley (Trentino - Italy): epidemiological study Rémi Sitta: Use of linked registries in the design of cohort studies, a tool against selection bias: the Constances example. P8.7 P8.8 P8.9 P8.10 Evaluating hospital performance Francesca Ieva: Mixed effect models for provider profiling in cardiovascular healthcare context Doris Tove Kristoffersen: The use of Kaplan-Meier plots when comparing hospital mortality. Doris Tove Kristoffersen: Accounting for patients transferred between hospitals when using mortality as a quality indicator for the comparison of hospitals. Ondrej Majek: Performance of Screening Colonoscopy Centres in a Nationwide Colorectal Cancer Screening Programme: Evaluation Using Hierarchical Bayesian Model Elisa Carretta: Hospital volume and survival from cancer surgery: the experience of a local area with an high incidence of gastric cancer Sarah Seaton: Tolerance intervals for identification of outlier healthcare providers: the incorporation of benchmark uncertainty. Maria Weyermann: Geographic variations in avoidable hospital admissions for asthma across Germany Federico Ambrogi: The comparative evaluation of Italian Regional Health System through PLS-SEM Richard Jacques: Casemix adjustment for comparing standardised event rates Silke Knorr: Probability of in-hospital mortality: analysis of administrative data in Germany P9 P9.1 Functional data analysis Stanislav Katina: Visualisation and spatiotemporal smoothing of single trial EEG data P10 P10.1 P10.2 Health economics and regulatory affairs Jasdeep. K Bhambra: Bayesian Evidence Synthesis in a Health Economic Model for Dementia Antoine Regnault: Analysis of time to patient-reported outcome meaningful change: Illustration from a clinical trial with catumaxomab in patients with malignant ascites Dorota Doherty: Prediction of pregnancy outcomes in planned homebirth Juan R Gonzalez: Multivariate latent class model for non-supervised classification in RNAseq experiments Werner Vach: The (little) need for and the (large) impact of post hoc application of formal criteria to check clinical relevance in well conducted RCTs P8.4 P8.5 P8.6 P10.3 P10.4 P10.5 P11 P11.1 P11.2 P11.3 P11.4 P11.5 P11.6 Incomplete data Danice Ng: Can the repeated attempts model help to fit MNAR selection models? Christele Augard: Using planned missing values in longitudinal trials to relieve patient burden and reduce costs Bola Coker: Regression models for repeated binary measures under different missingness assumptions Ákos Ferenc Pap: Drop out in randomized controlled non-inferiority trials with time to event outcome: a worst case sensitivity analysis using a Bayesian method Rory Wolfe: Dealing with missing data in the development and validation of clinical risk prediction models: is missing as normal ever a sensible strategy? Menelaos Pavlou: Contrasting Informative Cluster Size with Missing Data ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info P13 P13.1 P13.2 P13.3 P13.4 P13.5 P14 P14.1 P14.2 P14.3 P14.4 P14.5 P14.6 P14.7 P14.8 Latent variable models Baoyue Li: A Bayesian multivariate multilevel probit model applied to nursing burnout data Klaus Groes Larsen: Using mixture models for identification of typical trajectories of recovery in patients with Major Depressive Disorder Salma Ayis: Assessing Item Properties of the Hospital Anxiety and Depression Scale (HADS) for the Detection of Depression in Stroke Patinets using Item Response Theory (IRT) Lena Herich: Application of a Latent-Class-Survival Model for data of a cardiological trial Peter Brønnum: A Dynamic Prediction Model for Anticoagulant Therapy Longitudinal data R. Alonso: A new criterion for choosing the best working correlation structure in GEE analisys Marek Molas: Joint Hierarchical Generalized Linear Models Using H-likelihood Maria Josefsson: Causal inference with longitudinal outcomes and non-ignorable drop-out Duolao Wang: Assessment of Agreement between Digital 12-Lead ECG and continuous Holter ECG Recordings: A Heterogeneous Mixed Model Approach Ronald Geskus: A random effects model fitted to dichotomous outcome data with latent classes Eleni Rapsomaniki: Prognostic biomarkers across the patient journey: Systolic blood pressure before, during and after myocardial infarction. Malihe Nasiri: Discriminant analysis to predicting pre-eclampsia based on bivariate longitudinal biomarkers profiles Renata Majewska: Neonatal exposure to thimerosal from vaccines and child development in the first 3 years of life - application of generalized estimation equasions P15 P15.1 P15.2 P15.3 P15.4 P15.5 P15.6 Meta-analyses Yinghui Wei: A Bayesian approach for multivariate meta-analysis with many outcomes Joris Menten: Bayesian Meta-analysis of Diagnostic Tests Allowing for Imperfect Reference Standards Mercy Ofuya: Dichotomisation of continuous outcomes: A systematic review of meta-analyses using birthweight Catherine Klersy: Metanalysis of high quality observational studies: a surrogate for clinical trials? Ingeborg van der Tweel: Estimation of between-trial variance in sequential meta-analyses Sylwia Bujkiewicz: Multivariate meta-analysis of surrogate endpoints in health technology assessment: a Bayesian approach P15.7 Elinor Jones: Individual-level statistical analysis without pooling the data P15.8 Gang Li: Meta-analysis to estimate the treatment effect of Doripenem, Levofloxacin, and Imipenem-cilastin in complicated urinary tract infections P15.9 Eleni Rapsomaniki: An age-adjusted metric for risk discrimination, with application to age-specific cardiovascular disease prediction P15.10 Pablo Verde: Meta-analysis of paried-comparison studies of diagnostic test data: A Bayesian modelling approach P16 P16.1 P16.2 Model selection Abdel Douiri: Inverse problem within a regression framework Salma Ayis: Quantifying Bias due to Unobserved Heterogeneity at Individual and Cluster Levels when Using Binary Response Regression Models: A Simulation study Sunday, 19/7 Monday, 20/7 P12.3 P12.4 P12.5 Tuesday, 21/7 P12.2 Joint modelling of outcome and time-to-event Z.J. Musoro: A Simulation Study to Investigate The Performance of Frailty Variance Estimates in Repeated Events Data Chiara Brombin: Bayesian methods for joint modeling of longitudinal and survival data to assess validity of biomarkers in AIDS data Unfortunately, this poster has been withdrawn. Keith Abrams: Bayesian Modelling of Biomarker Data to Predict Clinical Outcomes Daniela Mariosa: Causal effects of Total Antioxidant Capacity intake on risk of postmenopausal breast cancer in a cohort study Wednesday, 22/7 P12 P12.1 Thursday, 23/7 P11.8 Helena Romaniuk: Multiple imputation in a longitudinal cohort study: a case study of sensitivity to imputation methods Tim Morris: Multiple imputation for an incomplete covariate which is a ratio Posters P11.7 25/156 Posters Thursday, 23/7 Wednesday, 22/7 Tuesday, 21/7 Monday, 20/7 Sunday, 19/7 26/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info P16.3 P16.4 P16.5 Sophie Swinkels: A map for the jungle of choices in Mixed Models for Repeated Measures Laure Wynants: Variable selection for prediction models based on multicenter data Javier Roca-Pardiñas: Generalized Additive Models and Computerized Breast Cancer Detection: Clinical Application P17 P17.1 Modelling in drug and device development Yuh-Ing Chen: Bioequivalence test based on a nonlinear mixed effect model for pharmacokinetic data in a 2x2 cross over design Ruediger Paul Laubender: Sample size and power considerations for estimating subject-treatment interactions in parallel-group designs using covariates Leslie Pibouleau: Development of a tool to elicit experts' beliefs for medical device evaluation P17.2 P17.3 P18 P18.1 P18.2 P18.3 P18.4 Modelling infectious disease Fabian Tibaldi: Model Based Estimates of Long-Term Persistence of Induced HPV Antibodies: A Flexible SubjectSpecific Approach Doyo Enki: Statistical models for biosurveillance: an empirical investigation Krisztina Boda: Epidemiological modelling of risk factors of human papilloma virus in women with positive cytology in the county of Csongrád Epidemiological modelling of risk factors of human papilloma virus in women with positive cytology in the county of Csongrád Achilleas Tsoumanis: An alternative to incorporate the number of persons tested when looking for time trends in STDs P19 P19.1 P19.2 Observational studies Geir Egil Eide: Attributable fractions of one year mortality after diagnosis of lung cancer Klaus Jung: Power Approximation for Logistic Regression Models with Multiple Risk Factors in Observational Studies P19.3 Elizabeth McKinnon: Analysis of clustered binary data with extreme proportions P19.4 Michel Hof: Multistage dynamic sampling design for observational studies P19.5 RHH Groenwold: Impact of measurement error and unmeasured confounding: a simulation study based on the example of ascorbic acid intake and mortality P19.6 Simona Littnerova: Propensity score: alternatives to logistic regression - real example P19.7 Edwin Amalraj Raja: A comparison of different multilevel models to analyse the effect of maternal obesity on pregnancy induced hypertension P19.8 Heather Murray: Use of Scottish Electronic Medical Record Linkage Systems: Illustrated by WOSCOPS 15 Year Follow-up Data P19.9 Paul HJ Donachie: Posterior Capsule Rupture complication rates for Cataract surgery from 1,173 ophthalmic surgeons in 28 UK NHS trusts. P19.10 Erik Berg: Paternal age - and the risk of oral cleft P20 P20.1 P20.2 P20.3 P20.4 Prediction Babak Choodari-Oskooei: A new measure of predictive ability for survival models Thomas Debray: Aggregating published prediction models with individual patient data Laura Bonnett: External Validation of a Prognostic Model in Epilepsy: simulation study and case study Siti Haslinda Mohd Din: Multiple longitudinal profiles of patients reported outcomes as predictors to clinical status of rheumatoid arthritis patients: A joint modeling approach P20.5 Kazem Nasserinejad: Using Dynamic Regression and Random Effects Models for Predicting Hemoglobin Levels in Novel Blood Donors P20.6 Jose A. Vilar: Time series clustering based on nonparametric multidimensional forecast densities: An application to clustering of mortality rates P20.7 Yohann Foucher: Time-dependent ROC curves for the estimation of true prognostic capacity of microarray data P20.8 David van Klaveren: Assessing discriminative ability in clustered data P20.9 Eric Ohuma: Modelling crown-rump length (CRL) data used for prediction of gestational age in early pregnancy when the data is truncated at both ends: The case study of INTERGROWTH-21st Project. P20.10 Jerome Lambert: Temporal profile of time-dependent discrimination measures in survival analysis P20.11 Rumana Z Omar: Validation of risk prediction models for clustered data: A simulation study and practical recommendations P21.5 P21.6 P21.7 P21.8 P21.9 P21.10 P21.11 P21.12 P21.13 P21.14 P21.15 P21.16 P21.17 P21.18 P21.19 P22 P22.1 P22.2 P22.3 Survival and multistate models Carlos Martinez: Bonferrini's method to compare k survival curves with recurrent events. Pierre-Jérôme Bergeron: One-Sample Test for Goodness-of-Fit for Length-Biased Right-Censored Survival Data. Geraldine Rauch: Planning and evaluating clinical trials with composite time-to-first-event endpoints in a competing risk framework Monday, 20/7 Tuesday, 21/7 Statistics for epidemiology Abdel Douiri: Re-sampling methods in prevalence and incidence studies Andreas Gleiss: Development of new Austrian height and weight references J Zhang: Exploring the Quality of Life in Patients with Suspected Heart Failure Wei-Chu Chie: Cultural vs. Clinical characteristics and health-related quality of life of patients with primary liver cancer by using the EORTC QLQ-C30 and the EORTC QLQ-HCC18 David Culliford: Exploring the use of body mass index as a covariate in survival models of total knee replacement Claus Dethlefsen: Assessing the effect of smoking legislation on incidence of cardiovascular diseases Mark Clements: New tricks for an old dog: using the delta method for non-linear estimators, with an application to competing risks in continuous time. Charlotte Rietbergen: Evaluation of the hierarchical power prior distribution. Christina Bamia: Exploring the estimator associated with the impact of a composite score of multiple binary exposures on continuous outcomes: An illustration using the Mediterranean Diet Score. Lesley-Anne Carter: Removal of bias from incidence trend estimation using excess zero models Anders Gorst-Rasmussen: Using the whole cohort when analyzing case-cohort data - some practical experiences Alexia Savignoni: Analysis methods comparison for censored paired survival data. A study based on survival data simulations with application on breast cancer. Sandra Waaijenborg: Identifying risk behavior for varicella infection using current status survival analysis Michal Abrahamowicz: When are interaction estimates confounded? Albert Sanchez-Niubo: Composite retrospective estimates of Drug Use Incidence from Periodic General Population Surveys in Spain. Giota Touloumi: Piecewise linear Poisson regression models with unknown break-points Antonio Gasparrini: A general conceptual and statistical framework for exposure-time-response relationships based on distributed lag non-linear models Jenö Reiczigel: On the validity of power simulation based on Fleishman distributions Agnieszka Kieltyka: Prenatal, perinatal and neonatal risk factors for autism. A case - control study in Poland Wednesday, 22/7 P21 P21.1 P21.2 P21.3 P21.4 Thursday, 23/7 P20.12 Kirsten Van Hoorde: Updating of polytomous risk prediction models based on sequential dichotomous modeling improves the performance P20.13 Sarah Seaton: Probability of survival for very preterm births: production and validation of a prognostic model. P20.14 Sophie Ancelet: Using Bayesian model averaging to improve radiation-induced cancer risks predictions P20.15 Hatef Darabi: Assessment of risk prediction and individualised screening of breast cancer among Swedish postmenopausal women. P20.16 Eva Janousova: Outcome prediction in schizophrenia patients based on image data P20.17 Ikhlaaq Ahmed: Meta-analysis methods for examining the performance of a predictive test: going beyond the average P20.18 Ben Van Calster: How to assess discrimination performance of polytomous prediction models: review and recommendations P20.19 Urko Aguirre: Comparison of two different modelling techniques to determine parameters related to changes in quality of life in colorectal cancer patients. P20.20 Ulla B Mogensen: Predictive performance of random forest based on pseudo-values P20.21 Verena Sophia Hoffmann: Finding cut-offs for continuous prediction models: an overview of methods and pitfalls P20.22 Gareth Ambler: Validating Prediction Models In Small Datasets P20.23 Paola M.V. Rancoita: Improving prognostic model development and assessment for survival data P20.24 Urko Aguirre: Comparison of Logistic Regression and Machine Learning methods: an application to the Colorectal Cancer stage prognosis. P20.25 Daan Nieboer: Dynamic updating of prediction models: how to deal with heterogeneity between settings Sunday, 19/7 27/156 Posters ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info 28/156 Posters Thursday, 23/7 Wednesday, 22/7 Tuesday, 21/7 Monday, 20/7 Sunday, 19/7 P22.4 P22.5 P22.6 P22.7 P22.8 P22.9 P22.10 P22.11 P22.12 P22.13 P22.14 P22.15 P22.16 P22.17 P22.18 P22.19 P22.20 P22.21 P22.22 P22.23 P22.24 P22.25 P22.26 P22.27 P22.28 P22.29 P22.30 P22.31 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info Rajvir Singh: Breast Feeding as a Time Varying - Time Dependent Factor for Birth Spacing: Multivariate Models with Validations and Predictions Pablo Martínez-Camblor: Expanded renal transplant: A multi-state model approach Christine Eulenburg: Fine and Gray approach versus cause-specific hazards: competing models or just two views of the same story? Zdenek Valenta: Autologous Stem Cell Transplant Study in Lymphoma Patients: Statistical Analysis of Multi-State Models Ian James: Exploratory survival analysis using longitudinal mixed-models Tomas Pavlik: Estimation of current cumulative incidence of leukaemia-free patients in chronic myeloid leukaemia Matthieu Resche-Rigon: Imputing missing covariate values in presence of competing risk David Dejardin: Frequentist Evaluation of Bayesian Methods for Survival Data Marie Vigan: Evaluation of estimation methods and tests of covariates in repeated time to event parametric models. Kristin Ohneberg: The Cumulative Proportional Odds Model for Competing Risks Catherine Quantin: Flexible modeling in Relative Survival:additive vs multiplicative model Sarah Seaton: Modelling discharge from a neonatal unit: an application of competing risks. Mark Rutherford: Using restricted cubic splines to approximate complex hazard functions in the analysis of time-toevent data. Nan van Geloven: Correcting for a dependent competing risk in the estimation of natural conception chances Michael Lauseker: Models for the Subdistribution Hazard of a Competing Risk under Left Truncation - a Comparison of two Approaches Pierre Joly: Predictions and life expectancies in Illness-death model Arun Pokhrel: Estimation of avoidable deaths based on the theory of competing risks Sally R. Hinchliffe: The Impact of Under and Over-recording of Cancer on Death Certificates in a Competing Risks Analysis: A Simulation Study Kym Snell: Modelling and utilising the baseline hazard in prediction models of clinical outcomes: a missed opportunity Markus Pfirrmann: Explaining differences in post-transplant survival between two studies in chronic myeloid leukaemia through identification of predictive factors by a Cox proportional hazard cure model Mathieu Bastard: The Use of Latent Trajectories in Survival Models to Explore the Effect of Longitudinal Data on Mortality Lucie Biard: Telling curative from palliative effects of covariates in prognostic analysis in a population with a cured fraction: Application of a biological cure model to metastasis-free survival in uveal melanoma patients. Nikos Pantazis: Performance of parametric survival models under non-random interval censoring: a simulation study Julie Boucquemont: The illness-death model to study progression of chronic kidney disease. Maria Kohl: Proportional and non-proportional subdistribution hazards regression with SAS Mathilda Bongers: Multistate modeling in the analysis of cost-effectiveness of NSCLC treatments Rebecca Betensky: Computationally simple estimation and improved efficiency for special cases of double truncation Mike Bradburn: Sample size calculation for time-to event outcomes in randomized controlled trials: An evaluation of standard methods ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info 29/156 Abstracts – Oral Presentations Sunday, 19 August – Pre Conference Courses Course 1 Analysis of rare genetic variants in common diseases (Full-day) Michael Nothnagel1 and Bettina Kulle Andreassen2 1 Institute of Medical Informatics and Statistics, Christian-Albrechts University, Kiel, Germany 2 Institute of Basic Medical Sciences (IMB), Department of Biostatistics, University of Oslo, Norway Technical progress has replaced decades-long scarcity by a close to overwhelming mass of genetic data. This wealth of data offers unprecedented chances for elucidating the role of genetic factors in the etiology of common diseases, but it also causes novel issues in the analysis. Genome-wide association studies (GWAS) of single-nucleotide polymorphisms (SNPs) in the DNA have been a standard approach to detect common genetic factors, i.e. those with a population frequency of at least one percent. Due to some inherent limitations of GWAS and with the advent of next-generation DNA sequencing (NGS) technologies, the focus has recently shifted to the analysis of rare variants with population frequencies less than one percent. The course will start with an introduction to basic ideas of genetic epidemiology, including relevant biological and genetic fundamentals. Concepts of and tools for GWAS will be covered in some detail in the second part, including a discussion of the pros and cons of this type of study. In a third part, we will focus on the analysis of rare variants, now routinely obtained from sequencing whole genomes or parts thereof. We will present different methods of variant collapsing and weighting in order to achieve reasonable power and ways of utilizing family pedigree information. Recent examples will be used for the motivation and illustration of these topics. Course 2 Bayesian computing with INLA (Half-day) Thiago Guerra Martins Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, Norway In these lectures, I will discuss approximate Bayesian inference for a class of models named `latent Gaussian models' (LGM). LGM's are perhaps the most commonly used class of models in statistical applications. It includes, among others, most of (generalized) linear models, (generalized) additive models, smoothing spline models, state space models, semiparametric regression, spatial and spatiotemporal models, log-Gaussian Cox processes and geostatistical and geoadditive models. The concept of LGM is intended for the modeling stage, but turns out to be extremely useful when doing inference as we can treat models listed above in a unified way and using the same algorithm and software tool. Our approach to (approximate) Bayesian inference, is to use integrated nested Laplace approximations (INLA). Using this new tool, we can directly compute very accurate approximations to the posterior marginals. The main benefit of these approximations is computational: where Markov chain Monte Carlo algorithms need hours or days to run, our approximations provide more precise estimates in seconds or minutes. Another advantage with our approach is its generality, which makes it possible to perform Bayesian analysis in an automatic, streamlined way, and to compute model comparison criteria and various predictive measures so that models can be compared and the model under study can be challenged. In these lectures I will introduce the required background and theory for understanding INLA, including details on Gaussian Markov random fields and fast computations of those using sparse matrix algorithms. I will end these lectures illustrating INLA on a range of examples in R (see www.r-inla.org). Required background: Basic knowledge of Bayesian statistics. Optional: Your own laptop with INLA preinstalled (www.r-inla.org). Course 3 Estimating treatment effects using longitudinal data (Half-day) Miguel Hernán Department of Epidemiology, Harvard School of Public Health, Boston, USA The availability and use of observational data---electronic medical records, claims databases, registries, etc. is increasing in medical research. However, a valid estimation of the causal effects of treatment from observational data requires strong assumptions regarding confounding and other potential biases. Estimating the effects of time-varying treatments in the presence of timevarying confounding factors also requires the use of appropriate analytic methods. The goal of this short course is to describe techniques for the estimation of causal treatment effects in longitudinal observational data. Course 4 Analysis of interval-censored survival data (Half-day) Philip Hougaard Lundbeck, Copenhagen, Denmark Interval-censored survival data occur when the time to an event is assessed by means of blood samples, urine samples, X-ray or other screening methods that cannot tell the exact time of change for the disease, but only that the change has happened since the previous examination. This is in contrast to standard thinking that assumes that the change happens at the time of the first positive examination. Even though this screening setup is very common and methods to handle such data non-parametrically in the one-sample case have been suggested more than 25 years ago, it is still not a standard method. However, interval-censored methods are needed in order to consider onset and diagnosis as two different things, like when we consider screening in order to diagnose a disease earlier. The reason for the low use of interval-censored methods is that in the non-parametric case, analysis is technically more complicated than standard survival methods based on exact times. The same applies to proportional hazards models. The talk will give an introduction to this type of data, including a discussion of the issues. The statistical theory will not be dealt with in detail, but high-level differences to results for standard rightcensored survival data will be presented. Both parametric, non-parametric and semi-parametric (proportional hazards) models will be covered. The talk will emphasize the applications, using examples from the literature as well as from my own experience regarding development of microalbuminuria among Type 2 diabetic patients. 30/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info Course 5 Latent class mixed models for longitudinal data and time-to-event data (Half-day) Hélène Jacqmin-Gadda1,2 and Cécile Proust-Lima1,2 1 2 INSERM U897, Bordeaux, France Bordeaux Segalen University, Bordeaux, France Latent class mixed models consist in exploring latent profiles of trajectories in heterogeneous population. They combine the random-effect models theory to account for the individual correlation in the repeated measures, and the latent class models theory to discriminate homogeneous latent groups when modelling trajectories of a longitudinal outcome. Extended to jointly model a longitudinal outcome and a time-to-event, they also provide a computationally attractive alternative to the standard joint modelling approach that are the shared random-effect models. The first part of this course will introduce the latent class mixed models, the estimation methods and the research questions they may address. The second part of this course will be dedicated to the joint latent class models. In addition to the theory, the estimation and the predictive dynamic tools that can be derived from them, a specific interest will be on methods to evaluate their goodness-of-fit and their predictive ability. Each concept will be illustrated through examples from cognitive aging studies as well as cancer studies. Finally, implementation and estimation of these models will be described within functions of the R package lcmm. ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info 31/156 Monday, 20 August Morning sessions (IP1, I1 , C1 – C4) IP1 Plenary session IP1 Concurrent control: key condition or sacred cow? Stephen Senn Competence Center for Methodology and Statistics, CRP Santé, Luxembourg Recent dramatic retractions in the field of genome-wide association studies have underlined the dangers of ignoring concurrent control and indeed the difficulties in establishing exactly what such control would be. On the other hand an often voiced criticism of clinical trials is that the obsession with concurrent control is wasteful of resources and delays progress in medical research. For the applied statistician, a key relevant concept in maintaining a balance between an obsessive and wasteful pessimism that maintains that no use can be made of historical controls, and a reckless optimism that courts mistaking noise for signal, is that of the bias-variance trade-off. The problem is that this is a concept that solves all difficulties in theory but gives no sure guide for how to proceed in practice. I shall consider the value of control using some simple frequentist and Bayesian models but also make liberal use of case studies. Amongst topics I aim to cover are how one should approach using historical controls and whether this should involve random effect models and if so how, network metaanalysis, the vexed question of versions of the treatment and the implications for blinding, add-on trials and medical decision- making in general. I shall also consider the ethical criticisms of concurrent control and will suggest that a Rawlsian approach to treatment entitlement suggests equipoise is irrelevant. I conclude that concurrent control is not essential for making some sort of an inference but is usually valuable if you want to make a good one. in Norway and to show the importance of timing of interventions. Reference: de Blasio BF, Iversen BG, Scalia Tomba G (2012) Effect of Vaccines and Antivirals during the Major 2009 A(H1N1) Pandemic Wave in Norway – And the Influence of Vaccination Timing. PLoS O50%ne 7(1): e30018. Doi:10.1371/ journal.pone.0030018 I1.2 Estimation of vaccine efficacy in a disease transmission framework using outbreak data Michiel van Boven, Susan Hahné, Helma Ruijs, Jacco Wallinga, Phil O'Neill Centre for Infectious Disease Control National Institute for Public Health and the Environment, Bilthoven, The Netherlands Vaccine efficacy is usually estimated by a comparison of the infection attack rates in vaccinated and unvaccinated persons (the cohort method), or by a comparison of the vaccination status of infected persons with the vaccination coverage in the population (the screening method). These methods are easy to apply but make the unrealistic assumption that every person has had a fixed amount of exposure, independent of the infection states of others. Here we present a method, based on infectious disease transmission models, to estimate vaccine efficacy together with the levels of exposure. We use a Bayesian framework which estimates the epidemiological parameters together with the (unobserved) infection graphs. The methodology is applied to ten outbreaks of mumps virus in primary schools in the Netherlas. The analyses show that (i) mumps virus is moderately transmissible in the setting of primary schools (R0hat=2.49; 95%CrI: 2.36-2.63), (ii) the vaccine is highly effective in preventing infection in a contact that would have resulted in transmission is the contacted individual I1 Modelling infectious disease was unvaccinated (VEhat=0.933; 95CrI: 0.908-0.954), I1.1 (iii) missing vaccination and infection information can be imputed Modeling disease spread for insight, foresight, hindsight... effectively, and Gianpaolo Scalia Tomba1, Birgitte Freiesleben de Blasio2 (iv) schools with only a handful of infections do not allow one to 1 estimate vaccine efficacy with any precision because escape from Dept of Mathematics, University of Rome Tor Vergata, Italy, 2Department of infection in vaccinated persons may have been caused by lack of Biostatistics,Institute of Basic Medical Sciences, University of Oslo, Norway exposure. I will discuss a number of open problems and directions for future developments. Mathematical models for disease spread can be simple or incorporate many details, build on realistic population structures and data or assume homogeneity, be stochastic or deterministic, admit analytical treatment or only I1.3 execution in computer simulations... Epidemiological and evolutionary dynamics of HIV-1 virulence All these variants may be studied for their one sake, but when some practical Christophe Fraser interpretation of results is required, it is desirable that a model be, in Einstein's Department of Infectious Disease Epidemiology, School of Public Health, words, "as simple as possible, but not simpler..." . It is then useful to distinguish Imperial College London, UK between possible purposes of modelling. Some models are constructed to study qualitative behaviour, to further insight into the factors that most influence Mathematical modelling has proven a powerful tool for integrating knowledge disease spread. Other models are formulated to predict e.g. how fast a new about the complex dynamics of HIV-1 at multiple scales. Here, I will set out a pandemic may spread over the world. But models may also be used to study hypothesis about the evolution of HIV-1 virulence that emerged from an what really happened during an epidemic and how successful interventions epidemiological calculation. I will then describe a series of recent studies that tested this hypothesis, and which the hypothesis passed. Several proposed really were. mechanisms of virulence evolution consistent with this hypothesis will be In the talk, a brief introduction to modelling disease spread will be given, explored. I will also speculate about consequences for public health, and more followed by an account of how modelling has been used to estimate how large specifically the evolutionary fate of virulence. the effect of antivirals and vaccination was during the 2009 A(H1N1) pandemic 32/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info References: Fraser et al, PNAS 2007; Hollingsworth et al, JID 2008; Hollingsworth et al, PLoS Pathogens 2010; Shirreff et al, PLoS Computational Biology 2011; Lythgoe et al, in press. C1 Causal inference I C1.1 Covariates and Confounding in Instrumental Variable Analyses Vanessa Didelez School of Mathematics, University of Bristol, Bristol, UK Instrumental variables (IVs) provide an approach for consistent inference on causal effects even in the presence of unmeasured confounding. Such methods have been used in the context of Mendelian randomisation, where the instrument is one (or several) genotype(s); as well as in pharmacoepidemiological contexts, where the physician's (or hospital's) preference is used as an instrument. In both situations it is common that covariates are available, even if these are deemed insufficient to adjust for all confounding. In this talk I will address the question of when available covariates must, can, or should better not be used in an IV analysis. Relevant issues are whether these covariates are effect modifiers, whether they are prior to or potentially affected by the instrumental variable, and whether they affect the strength of the instrument. The resulting biases or the loss/gain in efficiency will be illustrated by simulation studies. C1.2 Natural effect propagation Kjetil Røysland University of Oslo, Oslo, Norway The major role of statistics in clinical trials and epidemiological effect studies has been to carry out formal evaluations to judge whether we can "prove'' a treatment effect, and not so much "why'' there would be an effect. The latter question would involve a closer look at underlying mechanisms, or even chains of such. Much statistical analysis, however, treats biology as just a "black box''. In real life situations, for instance clinical trials, there is often more data available that could be used for the purpose of opening this black box. This could be through analyses of causal pathways and mediators, i.e. examination of how treatment effects propagate through the underlying system. In order to learn as much as possible from available data, there should be more attention in statistics to such causal exploration, not only to confirmatory analyses. The meaning of a mechanistic understanding, however, depends on the particular scientific setting. It is difficult to provide a general notion of pathway effects that fits to most scientific contexts. Judea Pearl's natural direct and indirect effects, based on nested counterfactuals, do provide a fairly transparent such notion. We will discuss the more general path specific natural effects in a formal way. These could be understood in terms of interventions or as signal sensing. We will furthermore consider examples with longitudinal data that are motivated by medical applications. a dichotomous quality indicator. Adjustment for differential case-mix between centers will be either based on standardization under a fixed or random center effects model which incorporates patient characteristics, on inverse weighting by the propensity to belong to the observed center (on the basis of patient characteristics), or using doubly robust estimation procedures which can be viewed as a compromise of both sets of approaches. We will discuss the relative advantages and disadvantages of the different approaches. Moreover, we will use simulation studies to evaluate their performance in terms of their ability to correctly classify centers of different quality, thereby focussing on realistic settings where some centers may contribute low numbers of patients. C1.4 Comparing population effects of different intervention policies, using a combination of inverse probability weighting and G-computation Saskia le Cessie1, Kim Boers1, Ben Willem Mol2, Sicco Scherjon1 1 Leiden University Medical Center, Leiden, The Netherlands, 2Academic Medical Center, Amsterdam, The Netherlands In clinical trials, the effect of an intervention is often compared to a ‘wait and see' policy. After the results of such a study are known, still questions can remain about the optimal strategy for different subgroups of patients, and the optimal timing to switch from ‘wait and see' to intervention. We encountered this in a clinical trial (DIGITAT) (Broers, BMJ, 2010) where induction of labor for pregnancies with suspected intrauterine growth restriction beyond 36 weeks gestation was compared with an expectant approach with careful surveillance. The study showed no significant differences in adverse neonatal outcome. However neonatal admissions were higher in the intervention group while infants were more growth restricted in the expectant group. This yielded the question whether there is an optimal timing of induction for these pregnancies. Here we discuss how population effects of different induction strategies can be estimated and compared (for example the effect of induction after a certain gestational age, or induction if the expected weight percentile is below a certain limit). This is done using causal methods for deriving optimal treatment regimes (Cain Int J of Biostat, 2010). Data of the DIGITAT trial and data of the women who refused to be randomized are used. We censor subjects when they are not following the proposed treatment regime and use a combination of inverse probability weighting and G-computation to estimate the outcome for women who are induced after a certain period of expectant management. Robust confidence intervals are calculated and compared with bootstrapped intervals. C1.5 Implementation of G-computation with complex longitudinal data Willem M. van der Wal, Rutger M. van den Bor, Kit C.B. Roes, Marinus J.C. Eijkemans Julius Center, University Medical Center Utrecht, Utrecht, The Netherlands When estimating causal effects using observational data, a correction for measured covariates that cause bias either through confounding, or by inducing missing values or dropout, can be made by fitting a marginal structural model (MSM). The most widely used method to fit an MSM is inverse probability weighting (IPW). As we will illustrate, IPW suffers from substantial small sample bias. An alternative to IPW that does not have this drawback is G-computation. However, the literature is lacking in describing how GC1.3 computation could be performed in a realistic complex longitudinal setting. We A comparison of statistical methods for benchmarking clinical centers in terms describe how G-computation can be performed in practice using readily of quality of care available standard software, extending the method that Snowden & Mortimer Machteld Varewyck, Stijn Vansteelandt, Els Goetghebeur (2011) presented for a simple point treatment setting. We will demonstrate the use of this method using a real data example from the field of nephrology, in Ghent University, Ghent, Belgium which both confounding and informative censoring is present. Inspired by a study on quality insurance of rectal cancer (Goetghebeur et al., 2011), we will discuss various statistical methods for benchmarking centers on J. M. Snowden, K. M. Mortimer (2011). Implementation of G-computation on a ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info 33/156 simulated data set: demonstration of a causal inference technique. American difference between the toxicity of the control and the combination to search for Journal of Epidemiology 173(7) 731-738. the MTD. C2.3 A Bayesian Approach to Dose-Finding Studies for Cancer Therapies: C2.1 Incorporating Later Cycles of Therapy Treatment Selection in Seamless Phase II/III Trials Incorporating Information Karen Pye, Anne Whitehead on Short-term Endpoints Lancaster University, Lancaster, UK Tim Friede1, Cornelia Ursula Kunz2, Nicholas Parsons2, Susan Todd3, Nigel Existing Bayesian designs for Phase 1 dose-finding studies can be based on Stallard2 fitting a smooth parametric curve to the relationship between dose and the risk 1 2 University Medical Center Göttingen, Göttingen, Germany, The University of of a DLT (Dose Limiting Toxicity). Prior information on model parameters are 3 Warwick, Coventry, UK, University of Reading, Reading, UK combined with binary observations of DLTs during the first cycle of therapy to Due to their potential to save development costs and to shorten time-to-market update the model parameters and to choose a safe dose to allocate to the next of a new treatment adaptive seamless phase II/III designs with treatment cohort. Results from just one cycle are used and later observations are selection at an interim analysis have become increasingly more attractive in ignored. recent years. If the primary endpoint is observed only after long-term follow-up To incorporate data from later cycles, a new approach based on intervalit may be desirable to use short-term endpoint data at the interim analysis to censored survival methods has been developed within a Bayesian decision select a treatment. Different methods have been proposed for selection of the procedure. This considers the relationship between the risk of a DLT during a treatment that will then continue along with the control group. If at least some particular cycle conditional on having no DLT in any previous cycle at that dose long-term endpoint data are available at the time of the interim analysis, they level, allowing for different risks of DLT in each cycle. The first cohort is might be used together with the short-term endpoint data to obtain an estimate assigned doses according to prior belief and the second cohort according to of the treatment effect upon which treatment selection can be based. prior belief plus responses of the first cohort in the first cycle. Allocation of Otherwise the treatment selection may be based on the short-term endpoint doses is then based on DLTs observed across all completed cycles for all data alone. While appropriate methods to combine pre and post adaptation subjects. data ensure control of the family-wise type I error rate in the strong sense, the power of the different approaches to selection of the best treatment differ A simulation study has been conducted to compare this new method with the depending on several assumptions. In this talk we present the results of a conventional approach for dose-finding. Results show that the intervalformal comparison of different methods for treatment selection based on censored survival model induces faster updating of the current estimate of analytical results and a simulation study, together with a summary of the MTD (Maximum Tolerated Dose) so that trials are generally shorter with fewer strengths and weaknesses of the approaches. Based on these results, we patients whilst keeping the same level of accuracy. C2 Adaptive clinical trials show how existing methods can be improved, increasing both the probability of selecting the most effective treatment and the power. C2.4 Bayesian Outcome-Adaptive Randomization in Clinical Trials C2.2 J. Jack Lee Comparative Bayesian escalation designs University of Texas MD Anderson Cancer Center, Houston, Texas, USA Emmanuel Lesaffre1,2, David Dejardin2, Paul Hamberg3, Jaap Verweij1 Outcome-adaptive randomization (AR) has been proposed in clinical trials to 1 Erasmus MC, Rotterdam, The Netherlands, 2KU Leuven, Leuven, Belgium, assign more patients to better treatments based on the interim data. Bayesian 3 Sint Franciscus Gasthuis, Rotterdam, The Netherlands framework provides a platform for continuous learning and, hence, is ideal for The primary objective of a Phase I dose escalation cancer study is to find the implementing AR. However, different views are still prevalent in medical and dose at which the drug will be tested in the subsequent phase II and III trials. In statistical communities on how useful AR really is. Clinical trials should be order to maximize the effectiveness of the treatment, drugs to treat cancer are designed with the goals of maintaining the type I error rate, achieving a combined with each other. The combination usually associates drugs that have specified power, and providing better treatments to patients both inside and outside the trial. Generally speaking, equal randomization (ER) requires a different mechanism of action against the disease. smaller sample size and yields a smaller number of non-responders than AR to The vast majority of the phase I dose escalation studies for single agents and achieve the same type I and type II errors. Conversely, AR produces a higher combination of agents implement a 3+3 dose escalation scheme to find the overall response than ER by assigning more patients to the better treatments MTD. This phase I design has been criticized for treating too many subjects at as the information accumulates in the trial. ER is preferred when the patient suboptimal doses and providing a poor MTD estimate. This design produces population outside the trial is large. AR is preferred when the difference in unreliable estimation of the true rate of toxicity at the optimal dose. One cause efficacy between treatments is large or when limited patients are available of unreliability is the nature of Phase I subjects: These subjects usually have outside the trial. The equivalence ratio of outside versus inside trial populations multiple tumor types and are in advanced stages of disease. Hence, the toxicity can be computed when comparing the two randomization approaches. levels observed in this population can not be generalized to the general Dynamic graphics and simulations will be presented to evaluate the relative population. merits of AR versus ER. A biomarker-based Bayesian adaptive design for We propose a randomized Bayesian dose escalation design for combinations selecting treatments, biomarkers, and patients for targeted agent development of drugs that takes advantage of the fact that a drug is added to a standard will be illustrated in the BATTLE trial for patients with non-small cell lung treatment to obtain, via Bayesian estimation, an improved estimation of the cancer. MTD and the toxicity level at the MTD. The proposed design implements a randomization between standard treatment C2.5 and the combination regimen for which we want the dose. We estimate the Phase I dose finding methods using longitudinal data and proportional odds 34/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info model in oncology method used by current DOC algorithms, especially for smaller CNVs. Adelaide Doussau1, Rodolphe Thiebaut1, Xavier Paoletti2 1 Bordeaux University Hospital, Univ. Bordeaux, Univ. Bordeaux, Bordeaux, C3.2 France, 2Curie Institue, Inserm U900, Paris, France Integration of multiple genome wide data sets in clinical risk prediction models Introduction: Stefanie Hieke1, Thomas Hielscher2, Richard F. Schlenk3, Martin Schumacher1, 2 3 4 In oncology, the optimal dose is typically defined as the dose associated with Axel Benner , Lars Bullinger , Harald Binder 1 some level of severe toxicity (DLT) during the first cycle of treatment, although Institute of Medical Biometry and Medical Informatics, University Medical toxicity is repeatedly measured, on an ordinal scale Center Freiburg, Freiburg, Germany, 2Division of Biostatistics, German Cancer Research Center, Heidelberg, Germany, 3Department of Internal Medicine III, Material and methods: University Hospital of Ulm, Ulm, Germany, 4Institute of Medical Biostatistics, We propose a new dose finding method using longitudinal measurements of Epidemiology and Informatics, University Medical Center Johannes Gutenberg, ordinal toxic side event using a proportional odds model (POM) to identify the Mainz, Germany optimal dose and to detect late drug effects. Optimal dose is then the dose producing a target rate of DLT per cycle. We compare the performances of our High-throughput microarray technology allows to measure various molecular approach to parallel-group design analyzed with a POM and to the continual features in parallel. Integration of such multiple genome wide data sets in risk reassessment method (CRM), and mimic real trials by introducing a censoring prediction models with regard to clinical endpoints could potentially help to process. Operating characteristics are mainly evaluated in terms of correct improve therapy management for future patients. We systematically investigate statistical strategies to connect several molecular sources with partial overlap identification of the target dose and power to detect time effect. in the biological samples. To take biological hierarchies into account, we adapt Results: an approach wich considers first one molecular source. We keep the After a mean sample size of 28 patients, estimates of the POM model can be information from this source fixed in the model when incorporating the second obtained in more than 95% of the simulations with substantial gains: In a source. We illustrate this strategy in an application to survival data from acute scenario without time effect, the target dose is recommended in 52% of the myeloid leukemia patients, considering microarray-based gene expression simulations with the usual CRM and between 66% and 76% of the simulations profiling and single nucleotide polymorphism microarrays with relatively small with the longitudinal POM model. In presence of strong time trend for the risk of overlap in the biological samples. While each of the molecular sources could toxicity (OR=1.6 per extra cycle), the power was greater than 85%. be considered as first or second source statistically, we will highlight how a Conclusions: particular combination corresponds to relevant biological questions. Using longitudinal POM is feasible in phase I dose finding trials in oncology, Specifically, certain molecular entities are seen to only emerge in clinical risk increases the ability of picking up the right dose and provides a robust tool to prediction signatures after taking the other molecular levels explicitly into account. These results indicate how in general statistical procedures can be detect late effects. adapted for connecting different molecular sources according to the underlying biology, resulting in a potentially improved basis for individual therapy C3 Bioinformatics management. C3.1 Challenges associated with detecting copy number variants using depth of C3.3 coverage with next-generation sequencing technology. Incorporation of Prior Biological Knowledge in Bayesian Variable Selection of Genomic Features Shu Mei Teo1, Yudi Pawitan3, Agus Salim2 1 NUS Graduate School for Integrative Sciences and Engineering, Singapore, Veronika Rockova1, Emmanuel Lesaffre1,2 Singapore, 2Saw Swee Hock School of Public Health, National University of 1Department of Biostatistics, Erasmus University, Rotterdam, The Netherlands, Singapore, Singapore, Singapore, 3Department of Medical Epidemiology and 2L-BioStat, Catholic University Leuven, Leuven, Belgium Biostatistics,Karolinska Institutet, Stockholm, Sweden Bayesian variable selection methods have now been customarily used to select features relevant/predictive for disease phenotype, primarily due to their Analyzing next generation sequencing (NGS) data for copy number variations flexibility in handling the ``large p small n" problem. In genomic applications we (CNVs) is a new and challenging field, with no standard protocols or quality are very often provided with complementary biological information regarding: controls measures. Depth of coverage (DOC) is one of the methods used to (a) the likelihood of association between the feature and the outcome (implied detect CNVs with NGS data- where a lower than expected DOC indicates e.g. by DNA characteristics when inferring regulatory mechanisms), (b) deletion and a higher than expected DOC indicates duplication. The algorithm evidence from previous studies, (c) grouping of functionally and biochemically relies heavily on the assumption that the sequencing process is uniform, but related predictors that belong to the same pathway. Here we set out to this has been shown to be unrealistic due to factors such as GC-content. Also, investigate how this prior information can be incorporated in Bayesian variable majority of current DOC algorithms require pre-binning the number of reads selection in a flexible manner. into non-overlapping windows of a fixed size. One problem is that the choice of We propose a hierarchical prior construction, which extends the Normal bin affects all downstream analysis and it is not clear if there is an optimal bin Exponential Gamma prior by letting the scale parameter to be dependent on a size. Using real data from the 1000 genomes project, we investigate whether stochastic linear combination of prior ``association scores". The purpose is to GC-content correction and pre-filtering reads using PHRED score will improve achieve sufficient adaptability, where coefficients of the features with higher sensitivity of DOC algorithm. We also introduce a novel concept of estimating ``likelihood of association" are shrunk to a lesser extent. Furthermore, variables DOC using per-base fragment count that avoids the need for data binning. within one pathway are allowed to share one common shrinkage parameter, The results show that GC-correction does not have much effect on sensitivity which encourages grouped selection and similarity of the estimated but it decreases the overall variance of the data and should improve specificity. coefficients. Filtering based on PHRED score does not seem to be crucial. Last but not We have developed a generalized EM algorithm for maximum a posteriori least, the fragment method has higher sensitivity as compared to the binning estimation, which offers huge computational savings as compared to the ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info 35/156 MCMC alternative. We have considered benefits of our proposed method in found in other sequencing approaches. detecting genes predictive of achieving complete remission in probit The proposed analysis is applied to a pooled set of 125 neuroblastoma regression, using information on pathway membership from online databases. samples. Second application deals with predicting functional targets of microRNAs, where prior information on the association is available from multiple online sources. C4 Multistate models C4.1 C3.4 Multistate models with multiple time scales Participant Identification in Genetic Association Studies: Methods and Practical Bendix Carstensen1, Simona Iacobelli2 Implications 1 Steno Diabetes Center, Gentofte, Denmark, 2Universita Tor Vergata, Roma, Nuala A. Sheehan, Nicholas Masca, Paul R. Burton Italy University of Leicester, Leicester, UK The normal approach to multistate modelling is to assume a common A recent method [1] was proposed that purported to detect whether a given underlying time-scale (Markov property) for all transitions between states. This individual contributed to a particular genomic mixture. This prompted grave greatly simplifies calculation of transition probabilities, because they all reduce concern about the public dissemination of aggregate statistics from genome- to multiplication of infinitesimal transition probability matrices, which are easily wide association studies and led to big changes in policy about general derived from fitted models for the transition intensities. This approach is accessibility to such statistics. It is of clear scientific importance that these data implemented in e.g. R-packages mstate and etm. be shared widely, but the confidentiality of study participants must not be However, in studies with progression through disease states it is untenable compromised. The issue of what summary genomic data can safely be posted from a clinical point of view to assume that transitions rates do not on the web is only addressed satisfactorily when the theoretical underpinnings (additionally) depend on either time of entry into a state or time spent in a state of the proposed method are clarified and its performance evaluated in terms of or even both. dependence on underlying assumptions. In this paper we will argue that whether this is the case or not, choice of The original method raised a number of concerns and several alternatives timescale(s) is an empirical question, not something to be decided a priori. have since been proposed including a simple linear regression approach [2]. Thus it should be routinely checked in multistate modelling which timescale(s) Here we suggest a generalised estimating equation (GEE) approach [3] provide the best description of the transition rates. enabling inferences that are more robust to approximation of the We will use an example from bone marrow transplant in leukaemia treatment to variance/covariance structure and can accommodate linkage disequilibrium. illustrate how this can be checked using simple parametric (spline) models for We affirm that, in principle, it is possible to determine that a ‘candidate' the rates, and how these models allow standard likelihood-ratio tests for the individual has participated in a study, given a subset of aggregate statistics relevant hypotheses. from that study. However, the methods depend critically on certain key factors We will demonstrate the necessary practicalities as well as final graphs needed including: the ancestry of participants in the study; the absolute and relative to convey the relevant clinical message. Moreover we will show how this type numbers of cases and controls; and the number of SNPs. of modelling both facilitates reporting of estimated rates as well as computation 1. Homer N et al (2008) PLoS Genetics 4(8):e1000167. of transition probabilities in the general case with multiple timescales influencing rates. 2. Visscher PM & Hill WG (2009) PLoS Genetics, 5(9):e1000628. 3. Masca, N et al. (2011) International Journal of Epidemiology 40: 1629- The emphasis of the talk will be on the practicalities in fitting the models and transforming the results into sensible reporting. 1642. C4.2 Risk factors of rehospitalisation and death for acute heart failure using multistate survival models Jiri Jarkovsky1, Simona Littnerova1, Jiri Parenica2, Marian Felsoci2, Roman Miklik2, Jindrich Spinar2 In genetics, massively parallel sequencing allows researchers to generate 1Masaryk University, Brno, Czech Republic, 2University Hospital, Brno, Czech large numbers of data in a fast and inexpensive manner. One attempt to make Republic optimal use of this powerful technique pools DNA samples before amplification and combines them into 3-dimensional designs, hoping to gain power for The acute heart failure (AHF) is serious syndrome with both high risk of detecting rare variants. These variants are important disease-causing alleles hospitalization mortality and low survival in follow-up. In addition to these under the 'rare variants, common disease' hypothesis. The correct analysis of terminal states there is after AHF also high risk of rehospitalisation for AHF, such data is not trivial however, as it ideally translates all steps of the pooling stroke and other serious but reversible events influencing quality of life of patients and financial demands of AHF treatment. The aim of our work was and amplification process into a statistical model. analyzed factors influencing risk of these multistate events and provide risk We present the most common data complications in such pooled samples, and stratification of patients after primo hospitalization for AHF using data from the ways of incorporating them in the data likelihood. Basic challenges originate Czech national AHF registry AHEAD. from the varying amplification factors, problems in base calling and the need to efficiently combine different sources of information (such as forward and The AHEAD Main registry includes 4,153 primo hospitalizations of patients reverse reads of DNA strands, different pools,...). We show how a variation on because of AHF from 7 centers in the Czech Republic with 24-hour cathlab maximum likelihood estimation leads to identification of the model parameters, service. Fully consecutive group of 623 patients dismissed after hospitalization allowing one to estimate the frequency of the variant in the population. We for AHF from university hospital Brno with median follow up 37.5 months were discuss the power of the design to detect variants, and compare it to the power used for the analysis of risk factors for rehospitalisation and death using multistate survival models. In the available data on subsequent hospitalization C3.5 Variant detection in 3D pooled DNA samples Bart Van Rompaye, Els Goetghebeur Ghent University, Ghent, Belgium 36/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info for cardiovascular event we found from 1 to 9 rehospitalisation in 22% of patients; the data were also verified against the Czech Database of Death Records a database administered by the Czech Statistical Office and 45.9% of patients deceased during the follow up were found. The Markov models with rehospitalisation for cardiovascular event as recursive state and death as terminal state were adopted for the analysis. properties to estimate survival curves for time to disability progression. In this talk, I present the transition model approach and discuss its advantages and drawbacks compared to survival analysis methods. I apply both methods to data from recent phase 3 studies that aim at quantifying the effect of fingolimod, the first approved oral treatment for relapsing remitting MS, on disability progression. Although the approaches are quite different in the outcome and data they use and in the interpretation of the estimated parameters, both showed a positive effect for the new treatment compared to C4.3 placebo, and moreover, survival curves obtained by the two methods were Semi-parametric Estimation of Quality Adjusted Lifetime Distribution in Semi- almost identical. Statistical issues such as robustness for model assumptions, Markov Illness-Death Models dealing with missing values and more will be discussed. Biswabrata Pradhan, Anup Dewanji Indian Statistical Institute, Kolkata, India C4.5 Quality adjusted lifetime (QAL) is an important measure, which incorporates both quality and duration of life, used for comparison of different treatment choices in many clinical trials. Estimation of QAL distribution is an important issue in such situations. In this work, we consider semi-parametric estimation of QAL distribution in different illness-death models. Hazard rates for the sojourn times are modelled using Cox's proportional hazards regression model. In the proposed approach, we write down the theoretical expression for the QAL distribution in terms of sojourn time distributions in each health state. The regression coefficients are estimated by maximizing the corresponding partial likelihood and the baseline cumulative hazards are estimated by using the method of Breslow. The estimate of QAL distribution is obtained by using these estimates in the theoretical expression of QAL distribution. By construction, this method gives a monotonic estimate of the QAL distribution. The asymptotic normality of the proposed estimator has been established. The performance of the estimator is judged by a simulation study. Proposed methodology is illustrated with the analysis of two data sets; Stanford Heart Transplant data and International Breast Cancer Study Group (IBCSG) Trial V data. C4.4 Estimating time to disease progression comparing transition models and survival methods Micha Mandel1, Francois Mercier2, Benjamin Eckert2, Peter Chin3, Rebecca Betensky4 1 The Hebrew University of Jerusalem, Jerusalem, Israel, 2Harvard School of Public Health, Boston, MA, USA, 3Novartis Pharma AG, Basel, Switzerland, 4 Novartis Pharmaceuticals Corporation, East Hanover, NJ, USA Standard approaches to estimate the time to progression of multiple sclerosis (MS) utilize survival analysis methods. These may be problematic for the typical data obtained in MS studies that comprise disability at only a few time points and assess change in only one direction (worsening). An alternative approach is to fit a Markov transition model and to use its special probabilistic Modelling Graft-versus-Host-Disease: statistical approaches incorporating clinical aspects Liesbeth C. de Wreede1, Johannes Schetelig2, Hein Putter3 1 Dept of Medical Statistics and Bioinformatics, Leiden University Medical Center/European Group for Blood and Marrow Transplantation, Leiden, The Netherlands, 2Medical Department I, University Hospital Carl Gustav Carus, Dresden, Germany, 3Dept of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands Graft-versus-Host-Disease (GvHD) is a serious complication after allogeneic hematopoietic stem cell transplantation (SCT). However, it is also positively associated with the ‘Graft-versus-Leukemia' effect, which represents the immunotherapeutic potential of SCT to prevent relapse of the malignant disease. Among statisticians, GvHD is a well-known example of an intermediate event, which is both analysed as an outcome in itself and as a time-dependent predictor of the competing events relapse (Rel) and nonrelapse mortality (NRM). We will present several issues to take into account in the analysis of these data. Firstly, we will explain how clinical considerations lead to different modelling choices and interpretations. Secondly, we will compare two statistical models and several refinements. The first of these is the Cox model for the cause-specific hazards for Rel and NRM in which GvHD is entered as time-dependent variable. This can be extended to a model with time-dependent effects of the occurrence of GvHD or to a dynamic landmarking approach. The second model is the multi-state model, in which, e.g., the probability of being alive with GvHD compared for patients with different baseline characteristics can be estimated. Such a model also allows to study new outcomes, such as "time spent free from GvHD-treatment and without relapse" as a measure of treatment success. In addition, the correlation between the occurrence of GvHD and Rel/NRM can be modelled by means of frailties. The models will be illustrated on two real datasets. Afternoon sessions (IP2, I2 , C5 – C13) days after discharge from hospital. I2 Evaluating hospital performance I2.1 Developing a summary hospital mortality index: how can we compare hospitals? A retrospective analysis of all English hospitals over 5 years Michael J Campbell, Richard Jacques, James Fotheringham, Jon Nichol, Ravi Maheswaran School of Health and Related Research, University of Sheffield, UK The Sheffield School of Health and Related Research were responsible for developing the Summary Hospital Mortality Index (SHMI) which is now in use for the Department of Health in the UK. For the first time an index was to be used that included all deaths in hospital, and deaths that occurred up to 30 The index was to be used to identify hospitals which have unexplained high mortality and perhaps should be investigated to see if the care they deliver is adequate. The model was developed from data over 5 years from Hospital Episode Statistics (HES) in England. There are a number of challenges to be overcome when developing the SHMI . The first is the size of data set (initially 96 million records). The next is what admissions to include (for example zero length of stay, maternity, palliative care). We then have to decide which type of model and which covariates to include to allow for case mix. We decided upon a logistic model nested within admission diagnosis. We also have to decide between direct and indirect standardisation and consider model fit. Then comes the question as to whether a hospital really is an outlier or merely extreme, for which we used funnel plots and random effects models. Finally we consider some of the ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info limitations of mortality indices and whether they can ever reflect the quality of care in a hospital. Reference : Campbell MJ, Jacques RM, Fotheringham J, Maheswaran R, Nicholl J (2012) Developing a summary hospital mortality index: retrospective analysis in English hospitals over five years. BMJ doi=10.1136/bmj.e1001 37/156 including age, sex and deprivation. Results: Elixhauser outperformed Charlson in all models. Discrimination (area under the ROC curve: c statistic) for Elixhauser for colorectal excision validated on data of a second year was 0.817 using regression and 0.817 (0.812) using SVMs with linear (Gaussian) kernel. Using a second year of admissions data for internal validation was vital as all models showed some over-fitting when validated against the same year (generally two to four percentage points). This was a particular issue, however, for unbalanced data using the nonlinear I2.2 kernels where differences in c-statistic could become much larger. In these Some statistical issues in identifying 'unusual' healthcare providers: cases dropping most of the “survivors” turned out to be the best option. The pattern was similar for pneumonia and AMI. multiple testing, regression-to-the-mean and outliers versus extremes In this talk I will discuss comorbidity scores and the differences between Hayley E Jones1, David J Spiegelhalter2 logistic regression and SVMs, before giving some results and the next steps, 1 School of Social and Community Medicine, University of Bristol, UK, which include and custom-built kernels that take into account the heterogeneity 2 Statistical Laboratory, University of Cambridge, UK and nature of the data and applying neural networks. Measures of the performance of healthcare providers are now commonly collected routinely at regular intervals over time. As well as monitoring overall trends and planning resources, it is of interest to identify individual providers C5 Clinical trials I that are potentially unusual, for example any with notably high or low event C5.1 rates during a particular time period, or that have experienced recent changes. As there are often large numbers of providers in such data sets, this procedure Linear Categorical Marginal Modeling of Solicited Symptoms in Vaccine Clinical should be automated. I will outline our approaches to dealing with three Trials statistical challenges that this presents. Wicher Bergsma1, Emmanuel Aris2, Fabian Tibaldi2 Firstly, I will demonstrate how control of the false discovery rate (FDR) can be 1London School of Economics and Political Science, London, UK, used to handle the intrinsic multiple testing problem. I will then review the 2GlaxoSmithKline Biologicals, Wavre, Belgium motivation for fitting hierarchical models to performance data of this type, which When developing new vaccines, it is necessary to show that new candidates provide shrinkage estimates of the performance of each healthcare provider. have an acceptable safety profile. Typically, the clinical safety evaluation of the Hierarchical modelling has become fairly well established in performance vaccine is performed regarding two specific aspects. First, the occurrence of a monitoring, but there is confusion in the literature as to how to identify unusual certain number of local or general symptoms is checked proactively via diary providers from such a model. I will examine two possible strategies, carefully cards recording the occurrence or absence of the symptom during a certain distinguishing between statistical ‘outliers’ and ‘extremes’, and highlighting a number of days after the injection. These symptoms are usually called solicited commonly used approach which we believe to be inappropriate. Finally, I will symptoms. For ease of recording a standard intensity scale is often used and demonstrate how tests for recent changes in the performance of individual contains a certain number of possible intensity of the symptom, typically units based on hierarchical models appropriately account for regression-to-thebetween 1 and 3. Subjects are then asked to fill in the maximum daily intensity mean, and consider the implications for statistical power. of each reported solicited symptom during a specific follow-up period. Second, Two case studies, mortality following heart surgery in New York State and the subject is also asked to record any occurrence of unsolicited symptoms Methicillin-resistant Staphylococcus aureus (MRSA) bacteraemia rates in which could also occur after vaccination. Analysis of the occurrence of adverse English National Health Service (NHS) Trusts, will be presented. events, and in particular of solicited symptoms, following vaccination is often needed for the safety and benefit-risk evaluation of any candidate vaccine. In this presentation, it will be shown that Linear Categorical Marginal Models are I2.3 well-suited to take the dependencies in the data arising from the repeated Traditional and machine learning methods for comorbidity adjustment in measurements into account and provide detailed and useful information for mortality risk models comparing safety profiles of different products while remaining relatively easy Alex Bottle to interpret. Linear Categorical Marginal Models will be presented and applied Dept of Primary Care and Social Medicine, Dr Foster Unit at Imperial College to a Phase III clinical trial of a candidate meningococcal pediatric vaccine. London, UK Background: Using outcome measures such as mortality for comparing C5.2 hospitals requires adjustment for patient factors, such as age and comorbidity, Variance estimation for propensity scores in randomised trials known as case-mix. Some comorbidity indices such as those by Charlson and Elizabeth Williamson1, Andrew Forbes2, Ian White3 Elixhauser are in common use but tend to simplify the relation with the 1 outcome and generally need recalibrating for a new data set. For binary Department of Epidemiology & Preventive Medicine, Monash University, and outcomes, case-mix adjustment is usually done using logistic regression; Melbourne School of2 Population Health, University of Melbourne, Melbourne, Preventive Medicine, machine learning methods such as artificial neural networks and support vector Victoria, Australia, Department of Epidemiology & 3 machines have shown promise and in principle are well equipped to explore Monash University, Melbourne, Victoria, Australia, MRC Biostatistics Unit, Cambridge, UK interactions between different comorbidities. Methods: For all hospital admissions in England for several conditions (e.g. Propensity scores were introduced as a tool for estimating effects of binary colorectal excision, AMI, pneumonia) we compared the model performance of treatments in non-randomised studies. The propensity score is the probability logistic regression and support vector machines for 30-day total mortality. For of receiving treatment conditional on measured confounding variables, and is SVMs, we tried different kernels (linear, low-order polynomial, Gaussian radial typically estimated from the data. We consider inverse probability weighting by basis function, and sigmoid). For comorbidity adjustment, we used i) the 17 the propensity score (IPW). Weights defined by the estimated propensity score Charlson components and ii) the 30 Elixhauser components in models also are used to create a population in which included confounders are balanced 38/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info between treatment groups. The IPW estimator is the regression coefficient for treatment from a regression model for the outcome on treatment, applying these weights as probability weights. In randomised trials confounding does not occur but IPW estimators can be used to adjust for chance imbalances of baseline prognostic variables. We consider a randomised trial with a continuous outcome measured at baseline and follow-up. We demonstrate that variances of IPW estimators that ignore the estimation of the weights, including those calculated by standard software, are typically far too large. We show that the variance can be correctly estimated using an extended sandwich estimator allowing for the uncertainty in the weights. Using the correct formula, the variance of the IPW estimator is comparable to the ANCOVA estimator. We derive similar results for binary outcomes. While non-convergence of outcome regression models estimating risk differences/ratios is commonplace, the IPW estimator can always be calculated. IPW estimators provide an attractive alternative to traditional analysis approaches for randomised trials. However, it is important to estimate the variance correctly; failure to do so may lead to over-inflated standard errors. C5.3 Properties of Estimators in Exponential Family Settings With Observationbased Stopping RulesProperties of Estimators in Exponential Family Settings With Observation-based Stopping Rules Elasma Milanzi1, Geert Molenberghs5, Ariel Alonso2, Michael G. Kenward3, Geert Verbeke6, Anastasios A. Tsiatis4, Marie Davidian4 1 I-BioStat, Universiteit Hasselt, Diepenbeek, Belgium, 2Department of Methodology and Statistics, Maastricht University, Maastricht, The Netherlands, 3Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London WC1E7HT, UK, 4Department of Statistics, North Carolina State University, Raleigh, NC, USA, 5I-BioStat, Universiteit Hasselt and I-BioStat, Katholieke Universiteit, Diepenbeek and Leuven, Belgium, 6IBioStat, Katholieke Universiteit and I-BioStat, Universiteit Hasselt, Leuven and Diepenbeek, Belgium Often, sample size is not fixed by design. A typical example is a sequential trial with a stopping rule, where stopping is based on what has been observed at an interim look. While such designs are used for time and cost efficiency, and hypothesis testing theory has been well developed, estimation following a sequential trial is a challenging, controversial problem. Progress has been made in the literature, predominantly for normal outcomes and/or for a deterministic stopping rule. Here, we place these settings in the broader context of outcomes following an exponential family distribution and, with a stochastic stopping rule that includes a deterministic rule and completely random sample size as special cases. We study (1) the so-called incompleteness property of the sufficient statistics, (2) a general class of linear estimators, and (3) joint and conditional likelihood estimation. Apart from the general exponential family setting, normal and binary outcomes are considered as key examples. While our results hold for a general number of looks, for ease of exposition we focus on the simple yet generic setting of two possible sample sizes, N=n or N=2n. PROspective Study of Pravastatin in the Elderly at Risk Trial (PROSPER) is used to illustrate the potential uses of record linkage of trial to routine data. PROSPER was a randomised double-blind trial of pravastatin vs. placebo in 5,804 men and women, aged 70-82 years, with a history, or at risk of vascular disease. Participants were recruited in Scotland, Ireland and the Netherlands. Within-trial average follow-up was 3.2 years. Statin treatment reduced the primary endpoint, coronary death, non-fatal myocardial infarction (MI) or fatal or non-fatal stroke, by 15% (hazard ratio (HR) 0.85, 95% CI 0.74 - 0.97, p = 0.014), did not reduce deaths or stroke but suggested an increased risk of cancer in the statin arm. In Scotland, 11,770 subjects were screened and 2,520 randomised. Record linkage follow-up was achieved for all subjects for mortality, incident cancers and all hospital discharge summaries. This provided a total of approximately 10 years follow-up. Analysis of the Scottish cohort over the entire follow-up found no evidence of a reduction in terms of all cause mortality (HR 0.99, 95% CI 0.88-1.11, p=0.87). However, coronary heart disease death or coronary hospitalisation was reduced (HR 0.81, 95% CI 0.69-0.94, p<0.0001) and overall there was no evidence of increased cancer risk. The presentation will describe the record linkage process, the results, a comparison of outcomes in randomised and non-randomised participants and an illustration of the use of these data to construct risk models in for elderly patients. C5.5 Estimating the optimal treatment effect when the randomised controlled trial design incorporates variable exposure to active intervention Chris Metcalfe University of Bristol, Bristol, UK In a randomised controlled trial where not all individuals have complied with their allocated intervention the unbiased intention to treat analysis will underestimate the treatment effect in the subset who do comply, and a per protocol analysis is not based on randomly allocated groups and is likely to be biased. This has motivated the development of unbiased estimators of the treatment effect, which compare compliers with the active intervention to a comparable sub-group in the control arm. There are trials where exposure to the active intervention varies by design. Randomised trials of screening for example, with two or more treatment options being randomly allocated in a nested trial amongst individuals found to have the disease. In such studies there is often a desire to conduct a secondary analysis which, continuing the example, estimates the optimal effect of screening in the situation when all those found to have the disease receive the treatment found to be most effective in the nested trial. This presentation will include a systematic review of the analytic approaches used in the published clinical literature, when the aim has been to estimate the optimal treatment effect in trials where exposure to the active treatment has varied by design. In addition the appropriateness and utility of extending estimators, developed to deal with non-compliance with randomly allocated active intervention, to trials where exposure to active intervention varies by design, will be evaluated in a simulation study and a real data example. C5.4 Use of record linkage to conduct long-term follow-up of a clinical trial and to investigate generalising cohorts to the underlying population Suzanne Lloyd1, Ian Ford1, David Stott2 1 Robertson Centre for Biostatistics, Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK, 2Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK Routinely collected health data are increasingly used in medical research. The C6 Epidemiological designs C6.1 Methodological challenges by the globolomic design - the Norwegian Women and Cancer postgenome cohort Eiliv Lund University of Tromsø, Tromsø, Norway ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info 39/156 The description of the genome in 2001 was anticipated to give a paradigmatic change in cancer research. In cancer epidemiology this lead to major efforts with gene-environment studies searching for risk related to single nucleotide polymorphisms, SNPs, and lifestyle. After a decade the findings have been scarce and the shift is towards functional studies i.e. transcriptomics or gene expression, epigenetics or microRNA and methylation, and finally proteomics. This all -omics approach is gaining momentum also in prospective studies using blood and information from time of enrolment in a multilevel design. The globolomic design of the NOWAC study adds to this complexity by the repeated measurements at time of diagnosis and from tumour tissues, all based on a nested case-control design. The design is dependent on the general hypothesis of blood as a communication channel for the carcinogenic process. The challenges to for the analysis of the globolomic design are many: 1. Define a carcinogenic model We suggest an extension of the case-time-control design which again is an extension of the case-crossover design. The basic idea in the case-crossover design is to use cases as their own controls by introducing a reference period back in time and assessing exposure status (e.g., drug use) in this period. Hereby one can address the problem of confounding by indication inherent in many epidemiological studies where drug prescription is dictated by the severity of the disease. However, general increase in drug usage in a population over time can bias the results. A possible solution is to use the casetime-control design where one, besides the cases, includes 'real' controls, still introducing a reference period for both the cases and the 'real' controls. It is then possible to model a linear increase in drug usage over time. We suggest a design where one introduces several reference periods for both cases and the 'real' controls. Besides the power gained, this allows us in a much more flexible way to model change in drug usage over time e.g, by a polynomial or a spline function. In addition, our model allows for individual random effects reflecting different personal disease severity. 2. Improve existing statistical methods to the multilevel, longitudinal An underlying assumption is that the probability of exposure in a given period is design independent of previous exposure conditionally on individual random effects. 3. Statistical testing of weak associations relevant to the How violations to this assumption affect our estimates is examined through carcinogenic process in the presence of stronger associations due large scale simulation studies, inspired by an example concerning antidepressant use and risk of out-of-hospital cardiac arrest (Weeke et al., to lifestyle factors Clinical Pharmacology & Therapeutics, in press). The challenges will be illustrated, but with no definite answers. C6.4 Inverse probability weighting for nested case-control studies: Application to a Analysis of case-cohort studies using flexible parametric models study of vitamin-D and prostate cancer. Anna Johansson1, Paul Dickman1, Therese Andersson1, Mats Lambe1, Paul Sven Ove Samuelsen, Nathalie C Støer, Haakon E Meyer Lambert1,2 University of Oslo, Oslo, Norway 1 Karolinska Institutet, Stockholm, Sweden, 2University of Leicester, Leicester, In nested case-control (NCC) studies controls are matched to cases of a UK, 3Regional Cancer Center of Central Sweden, Uppsala, Sweden disease on basis of at risk status and possibly other variables. Some expensive C6.2 The aim of this work is to develop flexible parametric survival models (FPM) using weighted likelihood for the analysis of case-cohort data. The case-cohort design was proposed by Prentice in 1986, and is particularly useful in studies where exposure information is difficult or expensive to obtain on a full cohort, e.g. biomarkers or medical records. To date, the main analysis tool for casecohort data has been weighted Cox regression, with weights accounting for the case-cohort sampling, yielding estimates of hazard ratios. Case-cohort data enables estimation of hazard rates since the design preserves information about the underlying cohort (person-time at risk). However, from a Cox model baseline hazard estimation requires kernel smoothing postestimation. Using weighted likelihood, we propose to use FPM for the analysis of casecohort data. The FPM uses restricted cubic splines to model the log cumulative hazard, thus the hazard rate is obtainable from model parameter estimates. One advantage of FPM is that it is easy to model time-dependent effects (nonproportional hazards). Measures such as time-dependent hazard ratios and rate differences can be constructed to quantify effects between groups. We show results from an analysis of education level and breast cancer incidence in Sweden. We compared weighted Cox regression to weighted FPM, and the results were similar for proportional models. However, the hazard ratio was strongly time-dependent (non-proportional), and the FPM fitted these models without requiring splitting of the timescale. In conclusion, the FPM provides a useful tool for the analysis of case-cohort data, particularly in large studies. C6.3 The case-time-control design with multiple reference periods Aksel Jensen, Per Kragh Andersen, Thomas Gerds University of Copenhagen, Copenhagen, Denmark exposure information is then obtained only for cases and controls. Traditionally such data are analyzed with Cox-regression stratified on matched case-control sets. If, however, we would like to analyze toward a subtype of the disease we would with the traditional method, due to the matching, have to discard the information from controls (and cases) for the other subtypes. An alternative is to estimate the probability of being selected as a control and include all available information using inverse probability weighting (IPW). Although this technique has been around for 15 years it has not been widely adopted. One reason may be that the estimation of inclusion probabilities becomes more complicated when there are more matching variables than just at-risk status. We apply IPW to a NCC study. Vitamin-D was obtained from stored blood samples for 700 incident cases of prostate cancer with one control per case, matched on at-risk status, age ( 6 months) and date of blood sampling ( 2 months). An important objective is also to investigate how vitamin-D is associated with death from prostate cancer and there were 160 such cases. We thus aim at making use of all collected exposure information, thereby increasing efficiency. The purpose of this talk is to present methods for including all matching variables when estimating weights in order to apply IPW for matched NCC studies. C6.5 Inverse probability weighting for nested case-control studies: A simulation study related to a nested case-control study of vitamin-D and prostate cancer. Nathalie C Støer, Sven Ove Samuelsen University of Oslo, Oslo, Norway In a related talk we presented methods for reusing controls in nested case- 40/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info controls (NCC) studies using inverse probability weighting (IPW) and applied these methods to a specific NCC with vitamin-D obtained from stored blood samples as exposure. Both incidence and death from prostate cancer were endpoints, but controls were matched to incident cases. Controls were also matched on age and calendar time of blood sampling. Since sampling of controls is only carried out once it is difficult to infer from this how IPW in general will work with additional matching. Thus we set up a simulation study based on the same cohort with respect to age and time of blood sampling. Levels of vitamin-D and times until cancer diagnosis and death of cancer were simulated in accordance with the real study and controls were sampled with the same matching criteria. IPW appeared to work well in the simulation mimicking the real study with respect to analyses towards both the incidence and death endpoints, and showed efficiency improvements for the death endpoint. In the real study age at blood sampling had an association with incidence, but was not correlated with vitamin-D. Calendar month of blood sampling was correlated with vitamin-D, but not the endpoints. It is important to investigate the robustness of IPW with respect to degree of dependency between matching variables, exposure and outcomes. Furthermore we studied how more closely matching and also batch effect (blood samples were analyzed in batches of 50) influenced the performance of IPW. Jennifer Rogers, Stuart Pocock London School of Hygiene and Tropical Medicine, London, UK A composite endpoint (i.e. time to first if several types of disease event) is commonly used as a primary outcome in clinical trials, as it increases event rates and combine multiple outcomes into one, avoiding issues of insufficient power and multiplicity. Heart failure is characterised by repeat hospitalisations for worsening condition, rendering such a composite endpoint suboptimal, as recurrent hospitalisations within individuals are ignored. Utilising all of the hospitalisations within individuals gives a more meaningful treatment effect on the true burden of disease. This talk presents statistical analyses of two heart failure clinical trials: ‘Eplerenone in Mild Patients Hospitalization and Survival Study in Heart Failure' (EMPHASIS-HF) and ‘Candesartan in Heart failure Assessment of Reduction in Mortality and morbidity' (CHARM). The datasets will be analysed using the Cox proportional-hazards model for the composite of first hospitalisation and cardiovascular death, and using the Andersen-Gill model and Negative Binomial generalised linear model for the repeat hospitalisations. The results of these analyses are compared and bootstrap simulations used to investigate statistical power of each method. We observe that analysing all hospitalisations within an individual gives significant improvements in statistical power. A comparison of hospitalisation rates in heart failure can be confounded by the competing risk of death. An increase in heart failure hospitalisations is associated with a worsening condition and a subsequent elevated risk of death. C7 Survival analysis I Analyses of recurrent events should take into consideration such informative C7.1 censoring. Statistical methodology being developed to jointly model New Method for Controlling for Unobserved Confounding in Time to Event hospitalisations and death will briefly be presented. Analyses of Comparative Effectiveness and Safety of Drugs Michal Abrahamowicz1, Lise Bjerre2, Yongling Xiao1 C7.3 1 McGill University, Montreal, Quebec, Canada, 2University of Ottawa, Ottawa, A multiplicative-regression model to compare the effect of factors associated Ontario, Canada with the time to graft failure between first and second renal transplant Unobserved confounding is the main source of bias in observational studies of Katy Trébern-Launay1, Magali Giral2, Yohann Foucher1 medication effects. Instrumental variables (IV) approach, that uses physicians' 1EA 4275 Biostatistics, Clinical Research and Subjective Measures in Health prescribing preferences as IV [1] can remove bias due to unobserved Sciences, Nantes University and Transplantation, Urology and Nephrology confounding, under certain assumptions [2]. However, the IV method is limited Institute (ITUN), INSERM U1064, Centaure, Nantes, France, 2Transplantation, to linear regression [1] and is not applicable in time to event analyses of Urology and Nephrology Institute (ITUN), INSERM U1064, Centaure, Nantes, prospective studies. France We propose a new, more general method to detect unobserved confounding of the estimated treatment effects and to reduce its impact. Similar to the IV Additive-regression models for relative survival are traditionally used in the methodology, we assume that the treatment of individual patients depends on evaluation of mortality related to chronic diseases and are usually based on the both: (1) (observed and un-observed) patients' characteristics and (2) expected mortality of general population (life table by sex, calendar year and subjective physicians' ‘prescribing preferences' [1;2]. We then propose a new age). To our knowledge, we propose for the first time to apply a multiplicativeconceptual framework, and derive (i) a test of the bias of the treatment effect regression model for relative survival (M-RS model) to analyze other outcome estimated in the conventional multivariable model, and (ii) a corrected than mortality. The clinical objective was to study the factors associated with estimator of the treatment effect that corrects for unobserved confounding. the time to graft failure (return-to-dialysis or patient death) of second kidney In simulations with strong unobserved confounding, the proposed test detected transplant recipients (STR) compared to first kidney transplant recipients the bias with > 90% ‘sensitivity', while ensuring an accurate type I error rate (FTR). 641 STR from the French DIVAT database between 1996 and 2010 (4.6%) when there was no confounding. The proposed corrected estimator of were analyzed. The expected graft failure hazard was estimated using a the treatment effect reduced the relative bias from 43% to 9%, and improved parametric proportional hazard (PH) model with a stepwise baseline function based on 2462 FTR. We carried out a multiplicative PH model with a stepwise the coverage rate from 11% to 91%. baseline function to estimate the STR relative risk of graft failure. Both models In conclusion, the proposed method may help detecting unobserved were estimated by maximizing their respective likelihood functions. confounding of the treatment effect estimates in observational studies of The hazard ratio (HR) for recipient ≥ 55 years versus < 55 years was 1.6-fold medications and largely reduce its impact. higher for STR compared to FTR (p=0.0387). Conversely, HR for donor ≥ 55 [1] Brookhart MA et al. Epidemiology, 2006;17:268-275. years versus < 55 years was 1.7-fold lower (p=0.0294) and HR for deceased [2] Abrahamowicz et al. Am J Epidemiology (AJE) 2011;174(4):494-502. versus living donor 3-fold lower (p=0.0332) for STR compared to FTR. While Cox model applied to STR and FTR did not offer original results according to the transplantation literature, the innovative use of M-RS model to study the C7.2 time to graft failure leads to new results useful for clinicians. Analysis of repeat event outcome data in clinical trials: examples in heart failure ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info C7.4 Risk assessment of time-varying factors on an acute event using the casecrossover method: A simulation study Peggy Sekula1, Martin Schumacher1, Maja Mockenhaupt2 1 Institute of Medical Biometry and Medical Informatics, University Medical Center Freiburg, Freiburg, Germany, 2Dokumentationszentrum schwerer Hautreaktionen (dZh), Dept. of Dermatology, University Medical Center Freiburg, Freiburg, Germany There are certain circumstances when the analysis of a case series might be benificial. For several reasons, such situation arose when studying the risk of drugs in severe cutaneous adverse reactions. The case-crossover method is one method to assess the risk of a time-varying factor on an acute event. In order to evaluate it in the practical setting, a reanalysis of existing data from a case-control study was done. Although it revealed reasonable estimates for several drugs, it also provided conspicuous results or no risk estimates for few drugs. While some discrepancies could be explained, the fact that case-crossover estimates were lower on average than case-control estimates could not fully be clarified. The objective was therefore to develop a simulation model for further assessment of the method to address questions raised by the application and beyond. Based on practical setting, the simulation model comprised a time-varying binary factor (=drug exposure) and a terminal event (=reaction). By assuming a Markov chain as the underlying stochastic model, simulation of the course of a population over time was possible from which subjects were drawn for casecrossover and case-control analyses. Several relevant scenarios and extensions such as the incorporation of a time-varying confounder were considered. We conclude that the risk of a time-varying factor using the case-crossover method could be reliably estimated when necessary requirements were correctly defined and exposure information was sufficient. Only in the settings of non-stationary exposure and of insufficiently informative exposure history, the case-crossover estimates revealed unacceptably high bias. C7.5 Analysis of complex correlated interval-censored HIV data from population based survey Khangelani Zuma Human Sciences Research Council, GAUTENG, South Africa In epidemiological of HIV, interval-censored data occur naturally. HIV infection time is not usually known exactly, only that it occurred before the survey, within some time interval or has not occurred at the time of the survey. Infections are often clustered within geographical areas such as enumerator areas and thus inducing unobserved frailty. In this paper we consider an approach for estimating parameters when infection is unknown and assumed correlated within an enumerator area. Dependency is modeled as frailties assuming a gamma distribution for frailties and a Weibull distribution for baseline hazards. Data from a household based multi-stage stratified sample design of 23 275 (96.0%) individuals from 10 584 households who were interviewed from whom 15 851 (65.4%) were tested for HIV is analyzed. BED capture EIA assays were used to test for recent HIV infection leading to interval censored data. Results show high degree of heterogeneity between enumerator areas indicating clustering of HIV infection and risk determinants by geographical areas. 41/156 Steffen Unkel1, Paddy Farrington1, Heather J. Whitaker1, Richard Pebody2 1 The Open University, Milton Keynes, UK, 2Health Protection Agency, London, UK In this talk, a unified frailty modelling framework is developed for representing and making inference on individual heterogeneities relevant to the transmission of infectious diseases, including heterogeneities that evolve over time. Central to this framework is the use of multivariate data on several infections. We propose new simple but flexible families of time-dependent frailty models, in which the frailty is modulated over time in a deterministic fashion. Methods of estimation, issues of identifiability and model choice are discussed. Results from such models are interpreted in the light of concomitant information on routes of transmission. Applications to paired serological survey data on a range of infections with same and different routes of transmission are presented. C8.2 Toward information synthesis with mechanistic models of HIV dynamics Prague Mélanie, Commenges Daniel, Thiébaut Rodolphe Univ. Bordeaux 2 ISPED INSERM U897, Bordeaux, France Parameters in mechanistic models based on ODE (Ordinary Differential Equations) have an intrinsic meaning. Thus, HIV modelling should lead to similar estimated values among clinical trials for some parameters such as the virus proliferation rate even if patients' histories and treatments differ. In the perspective of optimizing treatment, we aim to build a model which forecasts the patient treatment response in several studies. To validate it, we will present, in a Bayesian framework, a methodology for combined estimation of parameters over several clinical trials. We use the "Activated T cell model" with random effects on parameters. A pharmacodynamic function links the treatment dose to the effect of several antiretroviral drugs. To account for non-identifiability, a Bayesian approach allows introducing prior information using data from the literature. In view of the numerical complexity, we use a Maximum a Posteriori (MAP) estimator instead of classical MCMC. The EMRODE algorithm (Estimation in Models with Random effects based on Ordinary Differential Equations) allows computing the MAP. We analyse sequentially the different studies by taking as prior the updated posterior of previous analyses. We applied the methodology on two clinical trials (Albi ANRS 070: n=150 untreated patients starting dual nucleosides therapy and Puzzle ANRS 104: n=40 heavily pre-treated patients starting salvage therapy). Initial separate analyses show good prediction abilities of the model and fair agreement between parameters posterior distributions among studies. Combined analyses improve the fits and the predictions. C8.3 Modeling hepatitis C viral kinetics to compare antiviral potencies of two protease inhibitors: a simulation study under real conditions of use Cédric Laouénan1, Jérémie Guedj2, France Mentré1 1 Univ Paris Diderot, Sorbonne Paris Cité, INSERM, UMR 738, Paris, France and AP-HP, Hosp Bichat, Service de Biostatistique, Paris, France, 2Los Alamos National Laboratory, New Mexico, USA and Univ Paris Diderot, Sorbonne Paris Cité, INSERM, UMR 738, Paris, France 2011 marked a milestone in HCV therapy with the approval of two protease inhibitors (PI), telaprevir (TVR) and boceprevir (BOC), in addition to current treatment. Ongoing MODCUPIC-ANRS trial aims at providing estimation and comparison of potency of these drugs in real conditions of use. Objectives were to evaluate estimation performances for the chosen MODCUPIC's design C8 Modelling infectious disease and power to detect a difference of potency between both PIs using HCV dynamic model. C8.1 Time-varying frailty models and the estimation of heterogeneities in The biphasic viral kinetic model was used to characterize the changes in viral load levels, in which the drug potency, ɛ, represents the percentage of transmission of infectious diseases 42/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info blockage of virion production. We assumed εTVR=99.9% and εBOC=99%, 99.5% or 99.8%. We simulated 500 datasets using MODCUPIC's design (N=30 patients per PI and sampling at 0, 0.33, 1, 2, 3, 7, 14 days). Parameters were estimated by nonlinear mixed-effects models using the extended SAEM algorithm in MONOLIX v.4.1 that take into account below limit of detection data. For all parameters relative bias was <2% for fixed effects and relative root mean square errors was <5% for fixed effects and <35% for variances. Power to detect difference between ε was 100%, 100% and 94% with ε BOC=99%, 99.5%, 99.8% respectively. These powers remained very high in absence of datapoints at 0.33 and 1d (100%, 100%, 89.4%, respectively) or when N=10 per PI (100%, 99.8%, 62.2%, respectively). Compared with standard approach, modeling approach provides a more powerful tool to compare the antiviral potencies of TVR and BOC, even with sparse initial sampling or small number of patients. C8.4 was used, where health-care workers were the vectors transmitting CPKP from patient to patient. The model was simulated stochastically assuming Poisson rates over small time steps for the events included in the model and was fit to the cumulative number of CPKP cases over time to obtain monthly estimates of R0 (average number of secondary cases per primary case in the absence of infection control). It was then modified to account for the effect of infection control strategies. R0 was estimated equal to 2 in the peak months and the minimum hand hygiene compliance level necessary to control transmission was 50%. Simulations allowed to assess the impact of potential infection control measures on colonization prevalence and indicated that hand hygiene compliance rates higher than 50% should be coupled with measures such as CPKP screening and isolation/cohorting of colonized admissions. The present study is one of the few studies that employed mathematical modeling on surveillance data to estimate the R0 of a pathogen and to assess the impact of infection control strategies. Mathematical modeling may improve our ability to identify key parameters of the transmission process and design more effective infection control programs. Estimation of the basic reproduction number for infectious diseases with agevarying individual heterogeneity in contact rates IP2 Plenary session Paddy Farrington, Steffen Unkel, Heather Whitaker IP2 Open University, Milton Keynes, UK Epigenetics: A new frontier The basic reproduction number of an infectious disease, denoted R0, is the Terry Speed average number of secondary cases produced by a typical infectious individual in a susceptible population. Generally, the greater the value of R 0, the greater Walter & Eliza Hall Institute of Medical Research, Melbourne, Australia Apart from a few exceptions the DNA sequence of an organism, that is, its the overall vaccine coverage required to eliminate the infection. In populations comprising subgroups of individuals with different contact rates, genome, is the same no matter which cell you consider. If we view the genome for example age groups, R0 is the dominant eigenvalue of the next generation as a universal code for an organism, then how do we obtain cellular specificity? matrix, which describes contacts occurring within and between the different The answer seems to be via epigenetics, where the Greek prefix epi denotes subgroups. This definition has been extended to incorporate constant individual above or on top of, that is epigenetics is on top of genetics. If we think of the heterogeneity. We present a further extension to the more realistic situation genome sequence as the text, some people have likened the epigenome to the punctuation: the epigenetic marks on DNA help decide how the DNA text is where the individual heterogeneity is age-dependent. read. Epigenetics controls the spatial and temporal expression of genes, and is The model is formulated as an age-dependent frailty model for the effective also associated with disease states. It involves no change in the underlying contact rate. This gives rise to an age-dependent frailty model for the force of DNA sequence, and epigenetic marks are typically preserved during cell infection, which can be evaluated by confronting it with paired serological division. Epigenetic control occurs through different mechanisms, with DNA survey data on infections transmitted by the same route. methylation and histone modification being the principal ones. There is one For infections in endemic equilibrium, we obtain a simple algebraic expression major kind of DNA methylation in mammals, and several minor kinds, and for R0, which involves the left eigenvector of a ‘population' next generation dozens of types of histone modification. matrix and the equilibrium force of infection. We discuss the robustness of this Epigenetics has been studied in a low-throughput way for over 30 years, using estimator and the standard estimator, based more directly on the next a wide variety of tools and techniques from molecular biology, including DNA generation matrix, to bias or misspecification of the contact matrix. sequencing and mass spectrometry. With the advent of microarrays 15 years We illustrate the methods using serological survey data on parvovirus B19 and ago, these platforms began to be used to give high-throughput information on varicella zoster infection. We find that ignoring individual age-specific epigenetics. Methylation microarrays are now very widely used. In the last 5 heterogeneity can severely bias the estimated value of R0. years, second (also called next-) generation DNA sequencing has been used to study epigenetics, in particular using bisulphite-treated DNA or chromatin immunoprecipitation (ChIP) assays, each followed by massively parallel DNA C8.5 sequencing. There are now large national and international consortia compiling A mathematical model used as a tool to estimate carbapenemase-producing DNA sequence data relevant to epigenetics, and many statistical challenges Klebsiella pneumoniae transmissibility and to assess the impact of potential are arising. If we think of the single (reference) human genome, there will be interventions in the hospital setting literally hundreds of reference epigenomes, and their analysis will occupy V Sypsa1, M Psichogiou2, GA Bouzala2, L Hadjihannas2, A Hatzakis1, G Daikos2 biologists, bioinformaticians and biostatisticians for some time to come. This 1 Dept. of Hygiene, Epidemiology and Medical Statistics, Athens University talk will introduce the topic, outline the data becoming available, summarize Medical School, Athens, Greece, 21st Department of Internal Medicine- some of the progress made so far, and point to future biostatistical challenges. Propaedeutic, Laikon General Hospital, University of Athens, Athens, Greece Multiresistant pathogens in healthcare settings are emerging as a major public C9 Causal inference II health threat. Microbiological surveillance data collected in an observational study conducted during May 2009-June 2010 in a surgical unit were combined C9.1 with mathematical modeling to obtain estimates of carbapenemase-producing Simple estimation strategies for natural direct and indirect effects Klebsiella pneumoniae (CPKP) transmissibility and assess the impact of Stijn Vansteelandt1, Maarten Bekaert1, Theis Lange2 potential interventions. The Ross-Macdonald model for vector-borne diseases 1 Ghent University, Gent, Belgium, 2University of Copenhagen, Copenhagen, ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info Denmark The mediation formula has triggered an enormous progress on mediation analysis by enabling decomposition of a total effect into a (natural) direct and indirect effect (mediated through a specific mediator), regardless of the underlying statistical model. Current procedures calculate these effects through a combination of parameter estimates from a model for the mediator and outcome. However, their practical utility remains limited because of computational difficulties and the high dimensionality of typical results. van der Laan and Petersen addressed these concerns via parsimonious models for the natural direct effect. Tchetgen Tchetgen and Schpitser proposed estimators with desirable properties for the parameters indexing models with identity or log link, but their relative complexity continues to be a barrier for practical application. The focus of this talk will therefore be on richer model structures (e.g. logistic models) for natural direct and indirect effects. Easy-tocalculate estimators, obtainable via standard software, will be proposed and evaluated. Perspectives will be given on how to deal with exposure-induced mediator-outcome confounding. C9.2 Effective use of RPSFTM's in late stage cancer trials with substantial treatment cross-over. Jack Bowden, Ian White, Shaun Seaman MRC Biostatistics Unit, Cambridge, UK In late stage randomised controlled cancer trials, it is common to give the experimental treatment to control arm patients at the point of disease progression. This treatment switching (also called cross-over or contamination) can dilute the estimated treatment effect on overall survival. This can, in turn, impact the assessment of a treatment's benefit in future health economic evaluations. The rank-preserving structural failure time model (RPSFTM) of Robins and Tsiatis (1991) offers a potential solution to this problem; it can be used to estimate the causal effect of a treatment in an RCT allowing for treatment switching. The method requires specification of an ITT test and the log rank test is typically used. However, in the presence of substantial switching, it can have a low power since the hazard ratio is not constant over time. Schoenfeld (1981) showed that when the hazard ratio is not constant, a weighted version of the log rank test is more powerful than the standard version. This motivated us to develop a weighted log rank test statistic for the late stage cancer trial context, given working assumptions about the underlying hazard function in the population. We then explored the use of the weighted statistic within an RPSFTM analysis to estimate the causal effect of treatment. In simulations we found that this gave more efficient estimates of the causal effect. Furthermore, violation of the working assumptions was seen to only affect the efficiency of the estimates but not to induce bias. 43/156 Asymptotic approximations indicate that the smoothing parameters minimizing this mean squared error converges to zero faster than the optimal smoothing parameter for the estimation of the regression functions. In a simulation study we show that the proposed data- driven methods for selecting the smoothing parameters can yield lower empirical mean squared error than other methods available such as, e.g., cross-validation. C9.4 Estimating random center effects using instrumental variables Jozefien Buyze, Els Goetghebeur Ghent University, Gent, Belgium Instrumental variables allow to estimate causal effects of exposure on outcome in observational studies where it is unrealistic to assume all confounders have been measured. We utilize them here to study estimation of causal effects of care centers on outcome, accounting for different patient mixes over the centers. Efficiency however decreases substantially with increasing numbers of causal parameters. In line with association models in this field we therefore introduce random causal center effects, with a parametric assumption on their distribution. The two-stage model with standard mixed effects estimation in the second stage regression is not suitable. Since the level of clustering coincides with exposure here, i.e. care center, conditioning on the random effects introduces an association between the instrument and unmeasured confounders. Instead, we propose two approaches to estimate the random effects and their variance: 1) calculate the posterior distribution of the random effects conditional on the data and 2) maximize the joint density of the data and the random effects. Simulation results confirm that both approaches allow to correctly estimate the distribution of the causal center effects with smaller standard errors compared to the fixed causal effects model. Power to detect a deviant center may however decrease because of the shrinkage of random effects, especially for small centers. We discuss the practical relevance of the different balances sought for type I and type II errors in this setting. C10 Meta-analyses C10.1 Graph theory meets network meta-analysis Gerta Rücker University Medical Center Freiburg, Freiburg, Germany Network meta-analysis is an active field of research in clinical biostatistics, aiming at combining information from all randomised comparisons among a set of treatments for a medical condition. We show how graph-theoretical methods can be applied to network metaanalysis. A meta-analytic graph consists of vertices (treatments) and edges (randomised comparisons). First, we illustrate the full analogy between metaanalytic networks and electrical networks, where variance corresponds to C9.3 resistance, treatment effects to voltage, and weighted treatment effects to Targeted Smoothing Parameter Selection for Estimating Average Causal current flows. Based on this, we then show that graph-theoretical methods that have been routinely applied to electrical networks work also well in network Effects meta-analysis. Jenny Häggström, Xavier de Luna Umeå University, Umeå, Sweden In more detail, denote the edges by k, and let x=(xk)k and w=(wk)k be the The non-parametric estimation of average causal effects in observational vectors of observed effects and their inverse variances, respectively. Let y be studies relies on controlling for confounding covariates through smoothing the pointwise product vector of inverse variance-weighted observed effects regression methods such as kernel, splines or local polynomial regression. (wkxk)k. Using the edge-vertex incidence matrix B, we compute the Laplacian Such regression methods are tuned via smoothing parameters which regulates matrix L which plays a central role in spectral graph theory: the amount of degrees of freedom used in the fit. In this paper we propose L=BTdiag(w)B data-driven methods for selecting smoothing parameters when the targeted The resulting consistent treatment effects v induced in the edges can be parameter is an average causal effect. For this purpose, we propose to estimated via the Moore-Penrose pseudoinverse L+ of the Laplacian: estimate the exact expression of the mean squared error of the estimators. 44/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info v=BL+BTy For each pair (i,j) of treatments, the variance of the treatment effect is estimated in analogy to electrical resistance by Vij=L+ii+L+jj-2L+ij. We show that this method, being computationally simple, leads to the usual fixed effect model estimate when applied to pairwise meta-analysis and is consistent with published results when applied to network meta-analysis examples from the literature. Moreover, problems of heterogeneity and inconsistency, random effects modelling and including multi-arm trials are addressed. C10.2 Impact of Network Size and Inconsistency on the Results of MTC MetaAnalyses Ralf Bender1, Sibylle Sturtz1 1 Institute for Quality and Efficiency in Health Care (IQWiG), Cologne, Germany, 2 Medical Faculty of the University of Cologne, Cologne, Germany Mixed treatment comparison (MTC) meta-analyses, also called multiple treatment or network meta-analyses, are increasingly used in medical research. These methods allow a simultaneous analysis of all relevant interventions in a connected network even if direct evidence regarding two interventions is missing. The framework of MTC meta-analysis provides a flexible approach for complex networks. However, this method has yet some unsolved problems, in particular the choice of the network size and the assessment of inconsistency. We describe the practical application of MTC meta-analysis by means of examples. We focus on the impact of the size of the chosen network and the assumption of consistency. A larger network is based on more evidence but may show inconsistencies whereas a smaller network contains less evidence but may show no clear inconsistencies. A choice is required which network should be used in practice. In summary, MTC metaanalysis represents a promising approach, however, clear application standards are still lacking. Especially, standards for the identification of inconsistency and the way to deal with potential inconsistency are required. explored and illustrated for several published network analyses. This provides a starting point for more generally identifying conditions under which single edges can be identified as a source of incoherence. C10.4 Adapting Cochran's Q for Network Meta-Analysis Jochem König, Ulrike Krahn, Harald Binder Institute of Medical Biostatistics, Epidemiology and Informatics, Mainz, Germany When synthesizing results from clinical trials that compare two treatments by a meta-analysis, Cochran’s Q provides a well accepted tool for assessing heterogeneity between studies. In network meta-analysis, several treatments can be evaluated, where each individual study might consider only some of them. So far, Cochran’s Q has hardly been used in this setting. For investigating how Cochran’s Q could be useful for network meta-analysis, we consider a two step approach. First, a set of simple meta-analyses is performed for each pair of treatments where a direct comparison is available. Second, a reduced network-meta-analysis is based on effect estimates and standard errors of the first step. Cochran’s Q statistic for the whole network is seen to be the sum of squared Pearson residuals, and we furthermore show that it can be decomposed into a sum of within-edge Q statistics and a between-edges Q statistic. The latter allows for investigating potential incoherence of the network by inspecting its components. We illustrate the use of standard regression diagnostic tools for this. Specifically, all mixed treatment comparisons are shown to be a weighted mean between a direct and an indirect estimate. The weight of the direct estimate is identical to the hat matrix diagonal element, which is known as leverage in regression diagnostics. This is illustrated for a large network meta-analysis of antidepressants. There, the leverage diagnostic provides important insight into the network structure, which more generally highlights the usefulness of Cochran’s Q and its decomposition for network meta-analysis. C11 Evaluating hospital performance C11.1 C10.3 Model Selection for Locating of Incoherence in Network Meta-Analysis Ulrike Krahn, Jochem König, Harald Binder Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center Johannes Gutenberg University, Mainz, Germany Evaluation and comparison of the performance of Australian and New Zealand intensive care units Jessica Kasza1, John L. Moran2, Patricia J. Solomon1 1 University of Adelaide, Adelaide, South Australia, Australia, 2The Queen Elizabeth Hospital, Woodville, South Australia, Australia In a network meta-analysis, several treatments are compared simultaneously by connecting evidence from different randomized trials and allowing for indirect comparisons. While estimation assumes a coherent network of treatment effects, there might be some edges that lead to incoherence. We investigate how such edges can be identified in the context of fixed effect meta-analysis with known variances, using the inverse variance weighting method. The latter assumes normally distributed effect estimates for all studies and is generalized to network meta-analysis within the framework of general linear models. Analysis can be equivalently performed in two stages, first summarizing evidence for each possible treatment comparison, resulting in a direct edge effect with known variance, and secondly, fitting a linear model to the direct edge effects. We propose to explore the family of models that results from subsequently allowing more and more edges to have deviating direct effects. Both the amount of edge specific incoherence and the model fit after loosening edges can be assessed by chi-square tests. A further aspect is the detectable amount of local incoherence in a given network (with given variances of direct edge effects). It is shown to be a simple function of the hat matrix, the latter being itself a function of the observed known direct edge effect variances. Different methods to graphically display the results are Recently, the Australian Government has emphasised the need for monitoring and comparing the performance of Australian hospitals. Evaluating the performance of intensive care units (ICUs) is of particular importance, given that the most severe cases are treated in these units. Indeed, ICU performance can be thought of as a proxy for the overall performance of a hospital. We compare the performance of the ICUs contributing to the Australian and New Zealand Intensive Care Society (ANZICS) Adult Patient Database, and identify those ICUs with unusual performance. It is well-known that there are many statistical issues that must be accounted for in the evaluation of healthcare provider performance. Indicators of performance must be appropriately selected and estimated, investigators must adequately adjust for casemix, statistical variation must be fully accounted for, and adjustment for multiple comparisons must be made. Our basis for dealing with these issues is the estimation of a hierarchical logistic model for the inhospital death of each patient, with patients clustered within ICUs. Both patient- and ICU-level covariates are adjusted for, with a random intercept and random coefficient for the APACHE III severity score. Given that we expect most ICUs to have similar performance after adjustment for these covariates, we follow Ohlssen et al., JRSS A (2007), and estimate a null model that we ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info 45/156 expect the majority of ICUs to follow. This methodology allows us to rigorously C11.4 account for the aforementioned statistical issues, and accurately identify those Assessing Hospital Performance for Pneumonia Using Administrative Data ICUs contributing to the ANZICS database that have comparatively unusual With and Without Clinical Data: Does the Difference Matter? performance. Yew Yoong Ding1, John Abisheganaden1, Wai Fung Chong2, Bee Hoon Heng2, T K Lim3 C11.2 1 Tan Tock Seng Hospital, Singapore, Singapore, 2National Healthcare Group, Evaluating mortality rates for neonatal units using multiple membership models Singapore, Singapore, 3National University Hospital System, Singapore, Singapore Shalini Santhakumaran, Neena Modi, Deborah Ashby Imperial College, London, UK Introduction: With growing interest in hospital performance, the validity of In England, neonatal care is delivered via networks which are designed such outcomes evaluation across providers has never been more crucial. While that all services are provided within each network. Neonatal units are administrative data have typically been used for this purpose, uncertainty classified depending on the services they provide, and babies are often remains on whether incorporation of clinical data for risk adjustment can lead transferred for specialist care. The frequency of transfers and variation in to important differences in comparisons. We sought to examine this issue in the case-mix makes comparison of outcomes difficult. Previously, mortality has context of older persons hospitalized for pneumonia. been compared across networks rather than units to circumvent this problem, Methods: Using a retrospective cohort study design, we identified hospital though results are less useful for evaluating performance. Although statistical episodes for pneumonia among adults aged 55 years or older at 3 acute care methods exist to tackle these problems they are rarely used for benchmarking public hospitals over one year through their DRG and primary ICD-9-CM (480 in clinical practice. We compared a variety of hierarchical multiple membership to 486) codes. From these, 480 episodes from each hospital were randomly models to compare case-mix adjusted mortality whilst allowing for neonatal selected. Logistic regression models predicting 30-day mortality were constructed using: 1) Administrative data (demographics, admission transfers. Data were obtained from the National Neonatal Research Database, formed information, and comorbidity), and 2) Administrative and Clinical Data (severity from anonymised routine clinical data. Three-level Bayesian hierarchical of illness, pneumonia sub-type, and pre-morbid function). Corresponding multiple membership models were fitted to the data. A range of assumptions expected mortality, observed to expected ratio (O/E), and riskfor dependence and distribution of the unit and network parameters were used. adjusted mortality were computed for each hospital. Weights were assigned to the unit random effects based on the proportion of Results: Overall 30-day mortality was 23.5%, with unadjusted figures for the 3 time each baby spent in each unit. We adjusted for gestational age and birth hospitals being 22.1%, 26.3%, and 22.1%. Using administrative data alone, the weight, sex, use of antenatal steroids, maternal age and a multiple birth corresponding risk-adjusted 30-day mortality were 22.8%, 25.8%, and 21.6%, indicator to control for differences in case-mix. Models were compared using while with administrative and clinical data combined, these were 25.1%, 22.5%, DIC with consideration given to suitability of assumptions and interpretability of and 23.0% respectively. Hospital performance ranking reversed when both results. The posterior distributions of the unit and network effects and their data were combined. ranks were used to allow a neonatal unit to compare their performance with the Conclusion: Addition of clinical data to administrative data for risk rest of the country, within network and within unit classification. adjustment led to important differences in hospital performance evaluation for older persons admitted for pneumonia. This has implications on the practice of using administrative data alone. C11.3 Confidence intervals for ranks with application to performance indicators C12 Latent variable models Erik van Zwet1, Jelle Goeman1, Aldo Solari2 1 Leiden University Medical Center, Leiden, The Netherlands, 2University C12.1 Milano-Bicocca, Milano, Italy Extended likelihood approach to large-scale multiple testing Ranks are notoriously difficult to estimate, yet there is a growing demand to Youngjo Lee1, Jan Bjørnstad2 rank health care providers on the basis of certain "performance indicators". It is 1 Seoul National University, Seoul, Republic of Korea, 2Statistics Norway, Oslo, therefore very important to convey the uncertainty in a ranking to decision Norway makers by providing them with confidence intervals. We propose a method to construct confidence intervals for ranks that have either simultaneous or To date, only frequentist, Bayesian and empirical Bayes approaches have been individual coverage of 95%. Simultaneous confidence intervals are appropriate studied for the large-scale inference problem of testing simultaneously when, for instance, one is interested in the ten worst performing centres. hundreds or thousands of hypotheses. Their derivations start with some Individual intervals are appropriate as feed-back to a particular medical centre. summarizing statistics without modeling the basic responses. As a We contrast our approach with the Empirical Bayes (EB) method which is often consequence testing procedures have been developed without necessarily employed. Our approach is based on a fixed effects model and on testing checking model assumptions, and empirical null distributions are needed in multiple comparisons, while EB is based on a random effects model. Indeed, order to avoid the problem of rejecting all null hypotheses when the sample EB assumes latent centre effects which follow a normal distribution. This is a sizes are large. Nevertheless these procedures may not be statistically problematic assumption, which does not hold if there are centres that are truly efficient. In this paper we present the multiple testing problem as a multiple under or over performing. Application of the EB approach leads to shrinkage of prediction problem of whether a null hypothesis is true or not. We introduce the centre effects to a common mean. This has definite advantages, but also hierarchical random-effect models for basic responses and show how the produces some difficulties. Shrinkage introduces bias which is more severe for extended likelihood is build. It is shown that the likelihood prediction has a centres with fewer patients and this may not be fair. Also, since the amount of certain oracle property. The extended likelihood leads to new testing shrinkage varies from year to year, it is difficult to compare EB results across procedures, which are optimal for the usual loss function in hypothesis testing. years. Our approach is targeted to avoid these problems. We demonstrate on a The new tests are based on certain shrinkage t-statistics and control the local probability of false discovery for individual tests to maintain the global large data set from the Netherlands. frequentist false discovery rate and have no need to consider an empirical null 46/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info distribution for the shrinkage t-statistics. Conditions are given when these false rates vanish. Three examples illustrate how to use the likelihood method in practice. A numerical study shows that the likelihood approach can greatly improve existing methods and finding the best fitting model is crucial for the behaviour of test procedures. under-prediction of the disease risk. Finally, when the data are very sparse, as in our case, adding a non-spatial residual random effect may not be an optimal modeling choice. C12.4 An Investigation of Latent Class Trajectory Models of Prescribing to Define a C12.2 Phenotypic Marker of Disease Susceptibility Interpolation between spatial frameworks: an application of process Danielle Belgrave1, Iain Buchan1, Christopher Bishop2, Angela Simpson1, convolution to estimating neighbourhood disease prevalence Adnan Custovic1 1 Peter Congdon The University of Manchester, Manchester, UK, 2Microsoft Research Queen Mary University of London, London, UK Cambridge, Cambridge, UK Health data may be collected across one spatial framework (e.g. health provider agencies), but contrasts in health over another spatial framework (neighbourhoods) may be of policy interest. In the UK, population prevalence totals for chronic diseases are provided for populations served by general practitioner (GP) practices, but not for neighbourhoods (small areas of circa 1500 people), raising the question whether data for one framework can be used to provide spatially interpolated estimates of disease prevalence for the other. A discrete process convolution is applied to this end, and has advantages when there are a relatively large number of area units in one or other framework. Additionally the interpolation is modified to take account of observed neighbourhood indicators (e.g. hospitalization rates) of neighbourhood disease prevalence. These are reflective indicators of neighbourhood prevalence viewed as a latent construct. An illustrative application is to prevalence of psychosis in north east London, containing 190 GP practices and 562 neighbourhoods, including an assessment of sensitivity to kernel choice (e.g. normal vs. exponential). This application illustrates how a zero inflated Poisson can be used as the likelihood model for a reflective indicator. Background: The use of non-respiratory prescription drugs in early life may be a prognostic indicator of a child's susceptibility to asthma. Aim: To define the developmental trajectory of susceptibility in early-life based on patterns of prescriptions. Methods: We used data from the Manchester Asthma and Allergy Study (N=916), a prospective population-based birth cohort study designed to investigate disease development. We fit a taxonomy of longitudinal latent class models hypothesising subgroups of children who have changing levels of immune response over time under varying modelling assumptions. We assume that each child belongs to one of N latent classes, with the number of classes and their size not known a priori. Models were fit using Bayesian machine learning in Infer.NET. The models were compared for goodness-of-fit based on the model evidence which considers both model accuracy and generalizability. Survival models were used to address whether susceptibility, determined by patterns of prescription use, represented a higher risk of asthma severity. Results: The "best" model was a Hidden Markov Model which identified three latent classes of susceptibility: "Normal Response" (73.7%); "Medium Susceptibility" (22.6%) and "High Susceptibility" (3.7%). Children with "High Susceptibility" had a significantly higher hazard of experiencing asthma or wheeze symptoms within the first 3 years of life compared to those with C12.3 "Normal Response" (HR=4.23 95%CI 2.46-7.28, p<0.001) and those with Bayesian shared spatial-component models to combine sparse and "Medium Susceptibility" (HR=3.00, 95%CI 2.39-3.77, p<0.01). heterogeneous epidemiological data informing about a rare disease and detect Conclusion: By analysing trajectories of prescription use in early life, we spatial biases. obtain a phenotypic definition of susceptibility and subsequent development of Sophie Ancelet1, Juan José Abellan2, Sylvia Richardson3, Victor Del Rio Vilas 4, asthma. 5 Colin Birch 1 IRSN/LEPID, Fontenay-aux-Roses, France, 2Centre for Public Health C13 Functional data analysis/longitudinal data Research & CIBER Epidemiologia y Salud Publica, Valencia, Spain, 3Imperial College / Department of Epidemiology and Public Health, London, UK, C13.1 4 Department of Food, Environment and Rural Affairs, London, UK, 5Animal Parametric and non-parametric multivariate analysis of functional MRI data Health and Veterinary Laboratories Agency, New Haw, Addlestone, UK Daniela Adolf, Siegfried Kropf We propose several Bayesian shared spatial component models for the Otto-von-Guericke University, Department for Biometrics and Medical analysis of geographical disease risk informed by multiple, sparse and Informatics, Magdeburg, Germany heterogeneous disease surveillance sources. We do so first for one disease and then for two, possibly sharing environmental risk factors. Specifically, our Functional magnetic resonance imaging (fMRI) performs an indirect work is motivated by the analysis of the spatial variations of risk of two distinct measurement of neuronal activation in the human brain. The response signal is forms of scrapie infection affecting sheep in Wales using three heterogeneous a temporal series in single three-dimensional regions (voxels). These data are surveillance data sources. The aim is to hypothesize about the infectious or high-dimensional, because there are generally a few hundred scans (sample sporadic nature of each form of scrapie and to detect and discuss possible elements) but hundreds of thousands voxels (variables). Furthermore, the differences in the evidence provided by each surveillance source. We also measurements are correlated in space as well as in time. consider the problem of comparing the competing Bayesian hierarchical Analyses of these high-dimensional functional imaging data go beyond the models. In particular, we apply a mixed posterior predictive approach, compare scope of classical multivariate statistics. By default, fMRI data are analyzed different predictive scores and plot non-randomized PIT histograms to select voxel-wise on the basis of univariate linear models, using a pre-whitening the best predictive model. Our case study shows that using the proposed method to eliminate the temporal correlation. We adapt this strategy in a shared spatial component models improve the estimation of disease risks multivariate context applying so-called stabilized multivariate tests, which are compared to a separate analysis of each surveillance source. This also allows designed to cope with high-dimensional data and are based on the theory of detection of spatially structured biases and their correction to return the true left-spherical distributions. underlying spatial risk surface. Methodologically, we observe that There is always a need for approximation of the temporal correlation in this discrepancies between the observed data and the model assumption lead to ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info procedure. Usually, a first order autoregressive process is assumed for fMRI measurements and its correlation coefficient is estimated. We propose a nonparametric approach that renders an estimation of the temporal correlation structure unnecessary. Based on the stabilized multivariate test procedure, we use a block-wise permutation method including a random shift. A comparison of these methods using simulated data shows in detail the pros and cons of the test procedures. An application to real fMRI data illustrates the practicability of the multivariate approach, particularly when using the nonparametric proposal. C13.2 Path analysis with multilevel functional data: Change in glucose curves during pregnancy and its impact on birth weight. Kathrine Frey Frøslie1, Jo Røislien1, Elisabeth Qvigstad2, Kristin Godang3, Jens Bollerslev4, Tore Henriksen5, Marit Bragelien Veierød1 1 Department of Biostatistics, University of Oslo (UiO), Oslo, Norway, 2 Norwegian Resource Centre for Women's Health, Oslo University Hospital (OUH), Oslo, Norway, 3Section of Specialised Endocrinology, OUH, Oslo, Norway, 4Faculty of Clinical Medicine, UiO, Oslo, Norway, 5Division of Obstetrics and Gynaecology, OUH, Oslo, Norway Pregnancy is associated with increased insulin resistance, resulting in elevated blood glucose levels. High maternal glucose levels are known to increase the risk of several adverse pregnancy outcomes. Most studies of such effects are based on simple glucose measurements like the fasting glucose or the twohour value from oral glucose tolerance tests (OGTTs). However, simple measures might miss physiologically important information in OGTT glucose profiles. Our aim was to capture important information from entire OGTT curves during pregnancy and to use it in the analysis of neonatal outcomes. Functional data analysis includes methods for the analysis of multilevel curve data. We used multilevel functional principal component analysis (MFPCA) to analyse glucose curves from two visits during pregnancy in a Norwegian prospective cohort study of 974 healthy pregnant women. Almost all the variability in the curves was captured by two functional principal components (FPCs) at the visit specific level, and three FPCs at the visit/subject specific level. At both levels the physiologically useful interpretations of FPC1 and FPC2 were "General level" and "Time-to-peak", respectively. At the visit/subject level FPC3 represented oscillations. We further performed a Bayesian path analysis of the impact of early pregnancy body mass index (BMI) and OGTT curve features on birth weight. A simplified path model with BMI as an exogenous variable and FPC scores as intermediate variables was used. The implementation and interpretation of path models with functional data can be challenging, but add to the physiological understanding of mechanisms of obesity, glucose metabolism and neonatal outcomes. 47/156 analysis but landmarks contain only a very small proportion of data available from captured images. Anatomically defined curves have the advantage of providing a much richer expression of facial shape. This is explored in the context of identifying 24 ridge, valley or observed curves, which are automatically identified by (1) local extremes of surface curvature, (2) detection of surface slope discontinuities or (3) direct surface cuts in the normal direction. The P-spline approach to smoothing is then used to construct a set of semilandmarks on curves at any desired resolution. The penalty function in this type of smoothing can be adapted to the shapes of the curves to be identified. The shape of 57 3D stereophotogrammetric scans of human faces was determined by means of 30 anatomical landmarks and approximately 1000 equidistantly spaced semilandmarks on curves. The semilandmarks on the target shape were iteratively adjusted by bending energy to create geometrically homologous points with respect to a symmetrised reference shape. The bending energy between the reference and target shape was minimized and artificial deformation removed. In each step of the algorithm, Generalized Procrustes Superimposition optimized position, orientation, and scale of all shape coordinates. This resulted in Procrustes shape coordinates used in further analyses of multivariate variability and sexual dimorphism. The research was supported by Wellcome Trust grant WT086901MA. C13.4 Prediction of Visual Prognosis to Optimize Frequency of Perimetric Testing in Glaucoma Susan Bryan1, Koen Vermeer2, Hans Lemij3, Paul Eilers1, Emmanuel Lesaffre1,4 1 Erasmus Medical Center, Rotterdam, The Netherlands, 2Rotterdam Ophthalmic Institute, Rotterdam, The Netherlands, 3Rotterdam Eye Hospital, Rotterdam, The Netherlands, 4L-Biostat, Catholic University of Leuven, Leuven, Belgium Glaucoma is a leading cause of blindness in the world. Treatment slows the disease, possibly even halting disease progression. Our ultimate aim is to predict future field loss and gain knowledge about the individual's rate of progression in order to determine optimal treatment strategies. Our immediate aim is however to investigate the point specific evolutions over time. We use part of a unique database from the Rotterdam Eye Hospital in The Netherlands, focusing on 139 glaucoma patients followed for at least 10 years. The response variables are the 54 threshold points which describe the level of (differential light) sensitivity in each eye, measured from 0dB (blind) to 34dB (normal). Literature suggests modelling the response at each position over time and for every patient separately using linear, quadratic, exponential or tobit models. Some conclude that the exponential model performs the best, while others argue to use tobit models since there is censoring (at 0dB). Based on our data, we find that a linear model for the median fits best for 48.7% of the positions x C13.3 subjects, while the exponential model was for only 8.8% of the cases the best Automatic identification and analysis of anatomical curves across human face choice. Besides exploring other models, such as quantile models and various models that take censoring into account, we are currently developing a Stanislav Katina, Adrian Bowman hierarchical model that combines the evolutions of all positions. This is a The University of Glasgow, Glasgow,Scotland, UK challenging exercise that needs to take into account that glaucoma might not Identification of anatomical landmarks is a natural starting point for facial shape be present in both eyes at the start of the study but may develop over time. 48/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info Tuesday, 21 August Morning sessions (I3 - I4 , C14 – C21) consist in reconstructions of three-dimensional cerebral vascular geometries, I3 Functional data analysis I3.1 Functional data - an introduction towards applications Helle Sørensen Department of Mathematical Sciences, University of Copenhagen, Denmark Functional data are samples consisting of curves (or surfaces), usually with an implicit assumption of smoothness. Each curve is viewed as a single sample element rather than as a collection of sample elements. The talk will provide an introduction to the analysis of such data. Chromatography data and acceleration data from a study on horse gait will be used for illustration. The first obstacle with functional data is the fact that the curves are in practice only recorded discretely and with noise. This calls for smoothing, where the discrete data are converted to actual functional objects. Regularization methods, where smoothness is imposed on a basis expansion through penalization, are popular and will be reviewed in the talk. A second question is how to model relationships among several variables, when one or more of them are functional. In the talk we will give examples of regression models for functional data. The functional variables may enter as outcome, as predictor, or both, and the objects of interest are regression functions relating the predictor and the outcome. I3.2 A Modular Approach to Scalar-on-Function Regression Jeff Goldsmith1, Bruce Swihart2, Ciprian Crainiceanu2 1 Department of Biostatistics, Mailman School of Public Health, Columbia University, USA, 2Department of Biostatistics, Bloomberg School of Public Health Johns Hopkins University, USA We develop modular, computationally efficient methods for generalized functional linear models. The functional predictors are projected onto a large number of smooth eigenvectors allowing application to many real-data scenarios, and the coefficient function is estimated using penalized spline regression in a mixed model framework. The mixed model approach allows direct extension to functions observed longitudinally and facilitates the inclusion of non-linear effects of scalar covariates. Inferential techniques for functional covariates are developed and provide tools for model selection. We are motivated by a study of white matter demyelination via diffusion tensor imaging in which various cerebral white matter tract properties are used to predict cognitive and motor function in multiple sclerosis patients. All methods are implemented in the `refund' package available on CRAN. I3.3 A study of cerebral aneurysms pathogenesis: functional data analysis of three-dimensional geometries of the inner carotid artery Laura Sangalli MOX - Department of Mathematics, Politecnico di Milano, Italy I will describe some exploratory statistical analyses performed within the AneuRisk Project, a scientific endeavor that investigated the pathogenesis of cerebral aneurysms, in an interdisciplinary effort combining the experience of practitioners from neurosurgery and neuroradiology with that of researchers from statistics, numerical analysis and bio-engineering. The data analyzed obtained from angiographic images. Advanced techniques are developed for the statistical analysis of these complex functional data, including methods for multidimensional curve fitting, dimension reduction, registration and classification. The seminar is based on joint work with Piercesare Secchi, Simone Vantini, Alessandro Veneziani and Valeria Vitelli. C14 Clinical trials II C14.1 Efficient design of cluster randomized trials with treatment-dependent sampling costs and treatment-dependent unknown outcome variances Gerard van Breukelen, Math Candel Maastricht University, Maastricht, The Netherlands Cluster randomized trials are randomized experiments where clusters of persons are randomized to treatment, e.g. schools or general practices, and all persons sampled within a given cluster are given the same treatment. Published work on optimal design of cluster randomized trials provides sample size formulae (how many clusters, how many persons per cluster) as a function of sampling cost per cluster and per person, outcome variance, and intraclass correlation. These formulae are based on three restrictive assumptions: 1) an equal sample size per cluster, 2) homogeneous outcome variance and sampling costs, and 3) a known intraclass correlation (ICC), as the optimal design depends on this ICC and is thus a locally optimal design (LOD) only. The assumptions of equal cluster sizes and a known ICC were overcome in recent work. This presentation relaxes the assumptions of homogeneous outcome variance and sampling costs, by presenting Maximin designs (MMD) for treatment-dependent costs and treatment-dependent unknown outcome variances and ICC. MMD maximizes either the minimum efficiency or the minimum relative efficiency (relative to the LOD) over the plausible range of variance and ICC values. By choosing a larger or smaller range MMD allows balancing between efficiency and robustness. Graphs based on our equations will show how the optimal budget split between the two treatment arms, and thereby also the optimal sample size per arm, depends on the sampling costs and variance range per treatment arm at each design level (cluster, person). C14.2 Bayesian Phase II randomized design for time-to-event endpoint using historical control - Application to Oncology Daniel Lorand, Beat Neuenschwander, Rupam Ranjan Pal, Lanjia Lin Novartis Pharma AG, Basel, Switzerland Although single-arm Phase II clinical trials are still being widely used in the development of oncology drug, there are situations when randomized controlled trials are preferable. The use of such designs is especially adequate when a new drug is been combined with an approved drug or when the primary endpoint is a time-to-event endpoint such as progression-free survival. Although randomized Phase II trials are typically larger than uncontrolled trials, they should remain moderate in size so that reliable evidence on the efficacy of the investigated treatment can be obtained quickly at reasonable cost. Adequate use of historical control data which can done formally within the Bayesian framework can lead to highly efficient design. We describe such a design where clinical benefit is assessed by estimating the ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info hazard ratio between a new treatment and a control using a Bayesian cox model. A key feature of this approach is the use of an informative prior for the baseline hazard function in the control group through the implementation of a novel meta-analysis approach leading to suitable downweigthing of the historical information derived from published data. Operating characteristics simulated under a wide range of scenarios and compared to the standard frequentist approach shows improved false positive and/or negative rates. This illustrates that relevant historical data can be robustly incorporated into the design of randomized Phase II oncology clinical trials in a way that may either lead to smaller control arm or superior decision making when the sample size is fixed. C14.3 Evaluation of methods for design and analysis of cluster randomised crossover trials with binary outcomes with application to intensive care research Andrew Forbes1, Muhammad Akram1, Rinaldo Bellomo2, Michael Bailey2 1 Monash University, Melbourne, Victoria, Australia, 2Monash University and the Australia and New Zealand Intensive Care Society Centre for Outcome and Resource Evaluation, Victoria, Australia The assessment of interventions in intensive care research in Australia is hampered by the need for interventions to be applied at intensive care unit (ICU) level together with the limited number of intensive care units in Australia. As such, parallel-arm cluster randomised trials cannot be sized appropriately to detect small effects of universal ICU interventions on mortality such as intravenous caloric delivery in patients receiving nutrition or oxygen level targeting in mechanically ventilated patients. The increased efficiency of cluster randomised crossover designs presents an opportunity to remedy this situation, however the development and assessment of such designs in the literature has primarily been with continuous outcomes with associated linear mixed models rather than for binary outcomes. In this presentation we report on methods for design and analysis of cluster crossover trials with binary data. In particular we report on results from a simulation study based on the cluster size variability, size of period effects, and within- and between-period correlations observed in the Australian adult patient intensive care database. Using data generated from a marginal model, we report on appropriateness and modification of existing sample size formulae for Gaussian outcomes, as well as size, power and confidence interval coverage using a variety of cluster-summary and model-based methods. We also discuss the potential for extension to multi-period designs and their feasibility in the intensive care research setting. C14.4 Optimal target allocation proportion for correlated binary responses in a twotreatment clinical trial Atanu Biswas1, Saumen Mandal2, Camelia Trandafir3 1 Indian Statistical Institute, Kolkata, India, 2University of Manitoba, Winnipeg, Canada, 3Public University of Navarra, Pamplona, Spain Optimal allocation designs for the allocation proportion are obtained in the present paper for a two-treatment clinical trial, in the presence of possible correlation between the proportion of successes for two treatments. Possibility of such type of correlation is motivated by some real data. It is observed that the optimal allocation proportions highly depend on the correlation. We also discuss completely correlated set up where the binomial assumption cannot be made. C14.5 Statisticians Implementing Change and Cost Effectiveness in Clinical Trials Through Risk Based Prioritization Monitoring 49/156 Nicole Close EmpiriStat, Inc., Mt Airy, MD, USA In August 2011, FDA withdrew its 1988 guidance on "Guidance for the Monitoring of Clinical Investigations" and issued its draft guidance "Oversight of Clinical Investigations - A Risk-based Approach to Monitoring". The new guidance suggests risk based approaches, that the source data verification should be focused on critical fields (key efficacy and safety variables) and less than 100% source data verification (SDV) on less important fields may be acceptable. The guidance gives a clear signal that Sponsors are encouraged to explore the cost-effective ways to conduct the clinical monitoring instead of solely relying on the on-site monitoring. Statisticians now have an increasing role in centralized monitoring and should be prepared to tackle this area of expertise. A template for a Risk Based Statistical Monitoring Plan for Phase II/III clinical trials will be reviewed. Targeted key statistical metrics which identify areas for querying remotely and targeted onsite monitoring and additional training will be reviewed. Those metrics include defining outliers within sites and across sites, recruitment and follow-up rates, adverse event rates, and even modeling Investigator and staff resources, timelines and responsiveness for monitoring trial success. With EDC systems enabling centralized access to both trial and source data and the growing appreciation of the ability of statistical assessments, risk based plans are easier to write and execute. The amount of resources and time that can be saved by shifting away from 100% SDV and onsite monitoring, to a risk based approach, or even a hybrid approach is large for one clinical trial. The amounts saved over a clinical program is momentous. C15 Statistics for epidemiology I C15.1 Bias of relative risk estimates in cohort studies as induced by missing information due to death Nadine Binder1, Martin Schumacher2 1 Freiburg Center for Data Analysis and Modeling, Freiburg, Germany, 2 University Medical Center Freiburg, Freiburg, Germany In most clinical and epidemiological studies information on disease status is usually collected at regular follow-up times. Often, this information can only be retrieved in individuals who are alive at follow-up, but will be missing for those who died before. This is of particular relevance in long-term studies or when studying elderly populations. Frequently, individuals with missing information because of death are excluded and analysis is restricted to the surviving ones. Such naive analyses can lead to serious bias in incidence estimates, translating into bias in estimates of hazard ratios that correspond to potential risk or prognostic factors. We investigate this bias in hazard ratio estimates by simulating data from an illness-death multi-state model with different transition hazards, and considering the influence of risk factors following a proportional hazards model. We furthermore extend an approximate formula for the bias of the resulting incidence estimate by Joly et al. (Biostatistics, 2002) for a binary risk factor. Analytical and simulation results closely agree and reveal that the bias can be substantial and in either direction, predominantly depending on the differential mortality. In an application to a Danish long-term study on nephropathy for diabetics, where complete status information is available, we artificially induce missing intermediate disease status. The naive risk factor analyses differ significantly from those obtained in the original analysis and even change sign, giving further indication that the bias is relevant. This supports the analytical and simulation results and underlines that missing intermediate disease status cannot be ignored. 50/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info Medical college, Moshi/ Kilimanjaro, Tanzania, 3University of Bergen, Bergen/ Hordaland, Norway, 4Centre for International Health, Bergen/ Hordaland, Norway, 5University of Bergen, Bergen/ Hordaland, Norway, 6Department of Obstetrics and Gynaecology, Kilimanjaro Christian Medical Centre,, Moshi/ Kilimanjaro, Tanzania, 7Department of Obstetrics and Gynaecology, Kilimanjaro 8 Kilimanjaro Christian Both, the proportion of variation in outcome attributable to or explained by a Christian Medical Centre,, Moshi/ Kilimanjaro, Tanzania, 9 Medical college, Moshi/ Kilimanjaro, Tanzania, University of Bergen, Bergen/ prognostic factor (PEV; cf., e.g. Schemper, Stat.Med.2003), and the attributable Hordaland, Norway fraction or attributable risk (AR, cf., e.g. SMMR 2001, Issue 3), popular in epidemiology, take into account the prevalence of an exposure, or, more Objective: Perinatal mortality is as high as 5% in many countries in subgenerally, the distribution of a prognostic factor. They both aim at quantifying Saharan Africa. We compared the risk of a perinatal loss between women who ‘importance' or ‘relative importance' on a 0-1 scale. ‘Importance' (Healy, did, and women who did not lose their baby in a previous pregnancy. Stat.Med.1990; Nelson and O'Brien, JAIDS 2006) is seen as a function of the Methods: A total 19,811 women who delivered singletons for the first time at exposed fraction in a population and the strength of the exposure effect, as KCMC hospital between 2000 and 2008 were followed for a total of 4503 quantified by the relative risk. In this presentation we provide the first time a subsequent deliveries up to 2010. Women who had a multiple birth, or who systematic comparison of both concepts, analytically and empirically. In were referred from rural areas for various medical reasons were excluded. We particular we see that AR measures the degree to which an exposition (factor) estimated perinatal mortality in a subsequent delivery depending on the is necessary for disease or death, while PEV quantifies the degree to which outcome of the previous delivery. this exposition (factor) is necessary and sufficient. Both measures tend to 0 for an underlying relative risk tending to 1 or an exposure probability tending to 0. Results: A perinatal loss increased a woman's likelihood to be recorded with a However, as will be demonstrated, they are affected differently by changes in next pregnancy in our data from 19% to 31%. The recurrence risk of perinatal the distributions of outcomes and exposures. A large Swedish survey of death for women who had already lost one baby was 9.1% compared with a smoking and respiratory tract cancers is used to illustrate differences of AR and much lower risk of 2.8% for women who already had a surviving child, for a relative risk of 3.2 (95% CI: 2.2 - 4.7). Recurrence contributed 15% of perinatal PEV. deaths in all subsequent pregnancies. Preeclampsia, placental abruption, placenta previa, induced labour; preterm delivery and low birth weight in a C15.3 previous pregnancy were also associated with increased perinatal mortality in the next pregnancy. Quantifying bias in register based research Conclusions: Some women in Africa carry a very high risk of losing their child Luwis Diya, Kamila Czene, Marie Reilly in a pregnancy. Strategies of perinatal death prevention may attempt to target Karolinska Institutet, Stockholm, Sweden pregnant women who are particularly vulnerable or already have experienced a Aims: Register based research is important in understanding the burden of perinatal loss. disease in individuals, families and society at large. When registers are linked, their different start up dates leads to truncation of the individual's or family members' history. Despite a large volume of published research using these C15.5 registers the potential for bias in these results due to left truncation at register Luxemburg acUte myoCardial Infarction registry (LUCKY): estimation of the start-up has received very little attention. The aim of this study is to assess the effect of clinical and biochemical variables on the New-York Heart Association bias in familial risk estimates that use the Swedish Hospital Discharge score using penalized ordinal logistic regression. Register. Olivier Collignon1, Stephen Senn1, Michel Vaillant1, Yvan Devaux1, Marie-Lise Methods: As an illustrative example, we will study familial risk of acute Lair1, Daniel Wagner2 appendicitis, a well-defined diagnosis that is unlikely to be affected by changing 1CRP Santé, Strassen, Luxembourg, 2Institut National de Chirurgie Cardiaque patterns of diagnosis and treatment. Cases will be identified from the Hospital et de Cardiologie Interventionnelle, Luxembourg, Luxembourg Discharge Register and family relationships from the Multigenerational Register. Using the observed incidence rates of acute appendicitis and the Since 2006, every luxemburgish patient with acute myocardial infarction has Standardized Incidence rate (SIR) in family member of patients, we will been provided care at the INCCI (Institut National de Chirurgie Cardiaque et de simulate a virtual but complete population of Sweden using available vital Cardiologie Interventionnelle) and recorded in the LUCKY registry (Luxemburg statistics. We will investigate the magnitude of bias in various measures of acUte myoCardial Infarction). After discharge patients are followed up for familial relative risk: SIR, Incidence Rate Ratios and Hazard ratios. We will also several years by measuring some variables, like ischemic time, medication, investigate how the bias is affected by age at disease onset and the magnitude biomarkers, the occurrence of major adverse cardiovascular events and different indexes of remission of infarction. Especially, the NYHA (New-York of the familial relative risk. Heart Association) score, which ranges from 1 to 4 (from mild to severe) Conclusion: The proposed simulation tool can be used to assess the potential measures the limitation of patients' physical activity. Also we focused on for bias in linked register data, and thus facilitate sensitivity analyses. This tool determining which features in the registry were linked to an elevated NYHA is especially important for short life-span registers where the degree of score at the end of follow-up. Proportional odds ordinal logistic regression was truncation is more pronounced. performed to estimate the effect of each predictor on the NYHA score. To do C15.2 Explained variation versus attributable risk Michael Schemper Medical University of Vienna, Vienna, Austria C15.4 Recurrence risk of perinatal mortality in Northern Tanzania: A registry-based prospective cohort study Michael Johnson Mahande1, Anne Kjersti Dalveit1, Gunnar Kvaale4, Blandina Theophil Mmbaga1, Joseph Obure6, Gileard Masenga6, Rachel Manongi2, Rolv Terje Lie1 1 University of Bergen, Bergen/ Hordaland, Norway, 2Kilimanjaro Christian this, continuous variables were approached using restricted cubic splines and the equal slopes assumption was relaxed using a forward continuation ratio model. In order to propose an alternative to backward stepwise variable selection and to avoid overlearning due to high dimensionality, several penalization techniques were used. The final model was first validated by bootstrap and then simplified by determining a parsimonious submodel whose predicted probabilities correlated over 0.95 with those obtained with the full model. ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info C16 Joint modelling of outcome and time-to-event C16.1 (Student award winner) Dynamic Prediction Based on Joint Model for Categorical Response and Timeto-Event Magdalena Murawska1, Dimitris Rizopoulos1, Emmanuel Lesaffre1,2 1 Erasmus University Medical Center, Rotterdam, The Netherlands, 2I-Biostat Catholic University of Leuven, Leuven, Belgium In transplantation studies often categorical longitudinal measurements reflecting the status of the patient are collected for patients waiting for an organ transplant. In this setting it is often of primary interest to assess whether available history of the patient can be used for predicting patient survival as well as further performance on the waiting list. In this work we use a Bayesian approach to jointly model the performance of patients described by their categorical status that changes in time while waiting for the new organ together with the survival time on the waiting list. The model accounts also for the presence of competing risks due to the fact that patients are delisted from the list because of death, after transplantation or because of other reasons. In particular, the submodel for longitudinal categorical responses is a multinomial logit mixed-effects model, whereas for the event process we postulate the cause-specific hazard models that share the same random effects with the multinomial logit model. We illustrate how the fitted joint model can be used for the dynamic prediction of the cumulative incidence functions as well as the longitudinal response for patients based on their available longitudinal measurements of that response. C16.2 (Student award winner) Adjusting for measurement error in baseline prognostic biomarkers: A joint modelling approach Michael Crowther, Keith Abrams University of Leicester, Leicester, UK 51/156 biomarker information available up to time s, and they can be updated at each new measurement. In this presentation, we present two joint modelling approaches: the shared random-effect models that include characteristics of the longitudinal biomarker as predictors in the model for the time-to-event; and joint latent class models which assume that a latent class structure entirely captures the correlation between the longitudinal biomarker trajectory and the risk of event. We show how individual dynamic predictions can be computed from these two approaches and we detail methods to evaluate their predictive accuracy. Both approaches are illustrated and compared on datasets from prostate cancer studies where repeated measures of prostate specific antigen (PSA) and occurrence of clinical recurrence were routinely collected after the initial radiation therapy treatment. The objective from this study was to provide tools of early detection of prostate cancer clinical recurrence based on PSA trajectory. C16.4 A closed form likelihood for joint modelling of repeated measurements and survival outcomes, with an application to cystic fibrosis data. Jessica Barrett1, Robin Henderson2, David Taylor-Robinson3, Peter Diggle4 1 MRC Biostatistics Unit, Cambridge, UK, 2Newcastle University, Newcastle, UK, 3 University of Liverpool, Liverpool, UK, 4Lancaster University, Lancaster, UK We propose a joint model for repeated measurements and survival outcomes, which we the use to analyse data from the UK cystic fibrosis (CF) register. Cystic fibrosis is the most common serious inherited disease in Caucasian populations, and most people with CF die prematurely due to lung disease. The aim is to investigate the relationship between the longitudinal trajectory of lung function, measured as the forced expiratory volume (%FEV1), and survival in the UK Cystic Fibrosis Population. Our model is similar to the one proposed by Diggle and Kenward (Applied Statistics 43(1), 49-93), with a discretised time scale and a parametric probit model assumed for the survival distribution. We use recently developed distribution theory to calculate a closed form for the likelihood. Our method does not constrain the random effects part of the model, enabling us to compare models with different forms for the random effects. Estimates and confidence intervals for covariate effects and random effects parameters are calculated using maximum likelihood. C16.5 Joint Modeling of Repeatedly Measured Continuous Outcome and Intervalcensored Competing Risk Data Ralitza Gueorguieva1, Robert Rosenheck2, Haiqun Lin1 1 Yale University, New Haven, CT, USA, 2VA New England Mental Illness Research and Education Center, West Haven, CT, USA Methodological development of joint models of longitudinal and survival data has been rapid in recent years; however, their full potential in applied settings are yet to be fully explored. We describe a novel use of a specific association structure, linking the two-component models, and thus extend joint models to account for measurement error in a biomarker, even when only the baseline value of the biomarker is of interest. This is a common occurrence in registry data sources, where often repeated measurements exist but are simply ignored. The proposed specification is evaluated through simulation and applied to data from the General Practice Research Database (GPRD), investigating the effect of baseline systolic blood pressure (SBP) on the time to stroke in a cohort of obese patients with type 2 diabetes mellitus. By directly modelling the longitudinal component we reduce bias in the hazard ratio for the effect of SBP on the time to stroke, showing the large potential to improve In joint estimation of longitudinal data and dropout, using information regarding prognostic models which use only observed baseline biomarker values. the cause of dropout is likely to improve inferences for repeatedly measures outcomes and provide information about the association between causespecific dropout and the outcome process. A joint model that incorporates C16.3 cause-specific dropouts and allows for interval-censored dropout times is Dynamic predictions from joint models for longitudinal biomarker trajectory and proposed. This model includes a linear mixed model component for the time to clinical event: development and validation longitudinal outcome and competing risks models for the interval-censored Cécile Proust-Lima, Mbéry Séne cause-specific dropouts. We illustrate the model on data from the Clinical Antipsychotic Trials in Intervention Effectiveness (CATIE) study in INSERM, Centre INSERM U897, ISPED, Bordeaux, France schizophrenia. The results largely confirm the original CATIE findings but there In clinical studies, joint models can be used to describe correlated biomarker is indication that our proposed approach improves power for detecting trajectory and time to a clinical event. Recently, dynamic predictive tools were treatment differences over time. A limited simulation study demonstrates that derived from these models. They consist in the predicted probability of event in our method reduces bias in estimation of treatment effects compared to a window (s,s+t) given information on the biomarker up to the time of prediction treating all dropout as the same and to ignoring dropout. Important additional s. These dynamic predictive tools have two main advantages which make them advantages of our approach are that it allows estimation of the hazard function potentially very powerful tools for clinical decision making: they use all the for each specific dropout cause and can be fit in commercially 52/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info available software. C17 Genomics / system biology C17.1 High resolution QTL-mapping with whole-genome sequencing data Tomasz Burzykowski, Jurgen Claesen Hasselt University, Diepenbeek, Belgium during vaccination and 10 repeated measures were available after ATI. Several immune-related gene sets, such as the Interferon-gamma mediated signalling pathway, varied significantly after ATI and presented various dynamics over time. C17.3 Detecting genetic differences between monozygous twins by next-generation sequencing 1 , Patrik K.E. Magnusson1, Anna C.V. Johansson2, Lars Feuk2, Combining high-throughput sequencing technologies with pooling of Setia Pramana 1 segregants, as performed in bulked segregant analysis (BSA), can allow the Yudi Pawitan simultaneous mapping of multiple quantitative trait loci (QTL) present 1Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden, 2Dept. of Immunology, Genetics and Pathology, Rudbeck throughout the genome. In general terms, BSA consists of three main steps: Laboratory, Uppsala, Sweden 1. Controlled crossing of parents with and without a trait Monozygous (MZ) twins are derived from one fertilized egg, which is believed 2. Selection based on phenotypic screening of the offspring to result in identical DNA setup for the pair members. However, recent 3. Mapping of short offspring sequences against parental reference research shows that twins may not be 100% genetically identical. In this study the main aim is to investigate genetic differences between twins by identifying The final step allows detecting SNPs, insertions, and deletions. The occurrence discordant single-nucleotide variants (SNVs) of MZ twins. SNVs can be of such polymorphisms is a useful feature in order to identify regions, which are detected by using next-generation sequencing (NGS) methods which are now possibly related to the differences in traits. extensively used in the genomics studies since they provide cost efficient and BSA produces data in the form of binomial counts indicating how many reliable large scale DNA sequencing. However, NGS data are known to suffer offsprings have got the same/different nucleotide as the reference parent at a from high error rates due to many factors, e.g., base-calling and alignment particular location at the genome. By analyzing trends in the counts, as a errors. This makes identifying true SNVs in whole-genome sequencing to function of the location, regions, which might contain a gene related to the trait remain a challenging task. of interest, might be discovered. Here we propose procedures in order to filter out noise of NGS data, and We propose the use of a Hidden Markov Model (HMM) to identify the regions detect true discordant SNVs of monozygotic twins. The procedures start with of interest in the genome. The model includes several states, each associated filtering on coverage depth and phred quality scores in order to reduce basewith a different probability of observing the same/different nucleotide in an calling errors. Then to discover discordant SNVs between twins, Yates's chioffspring as compared to the parent. After estimating the model, the most squared test is implemented. To selectively remove false discordances probable state for each SNP can be selected. The most probable states can additional filtering procedures based on proximity to indels (insertions and then be used to indicate regions in the genome with a high probability of deletions) and neighboring SNVs, as well as minor allele frequency (MAF) are nucleotide (dis)similarity, i.e., which may be likely to contain trait-related genes. applied. The detected discordant SNVs are then validated using independent methodology. These procedures are applied to NGS data of 6.9 million variants from two identical twins using SOLiD and Illumina short-read sequencing C17.2 technologies. Application of Gene Set Analysis of Time-Course gene expression in a HIV vaccine trial C17.4 Boris Hejblum1, Jason Skinner2, Rodolphe Thiebaut1 1 Univ. Bordeaux, ISPED ; INSERM, Centre INSERM U897, F-33000 Bordeaux, Modeling count data in RNA-seq experiments using the Poisson-Tweedie family of distributions France, 2Baylor Institute for Immunology Research, Dallas, TX, USA Mikel Esnaola, Juan R Gonzalez Transcriptional profiling of human immune responses during the course of vaccination yields highly dimensional, information rich, data sets requiring Center for Research in Environmental Epidemiology (CREAL), Barcelona, analysis tools capable of both high sensitivity and the ability to bring functional Spain meaning to statistically derived results. Gene Set Analysis has emerged as a High-throughput RNA sequencing (RNA-seq) is used to detect genes that are standard method to accomplish such analyses. Here we extend this tool to differentially expressed among conditions. After some pre-processing steps longitudinal gene expression analysis, accounting for repeated measurements, that include alignment of the sequenced reads against a reference genome to attain a systems-level interpretation of dynamic immune responses in the and their posterior summarization into features of interest (e.g., genes), raw course of a therapeutic HIV vaccine trial. RNA-seq data is transformed into an initial table of counts. Typically, the Through the use of generalized additive mixed modeling and of the maxmean Poisson and negative binomial distributions have been used to anlayzed RNAstatistic (Efron et al, 2007), we assess the significance over time of predefined seq experiments. We show, however, that the rich diversity of expression gene sets and estimate their respective dynamics. Our modelling accounts for profiles produced by extensively-replicated RNA-seq experiments requires changes inside a significant genetic pathway, which can be heterogeneous, additional count data distributions in order to capture the gene expression dynamics revealed by this technology. We provide a new method for differential either in direction or in time. The DALIA-1 trial is a phase I/II trial evaluating the safety, the immunogenicity expression analysis implemented in a package for R called {\tt tweeDEseq}, at {\tt http://www.bioconductor.org/packages/release/bioc} and the impact on viral dynamics of a Dendritic cells based vaccine in 19 HIV available {\tt/html/tweeDEseq.html}. This is based on a broader class of count-data infected patients. Vaccination was performed during the first 16 weeks and models that permits different distributional assumptions for different genes and antiretroviral treatments were interrupted at 24 weeks for 24 weeks or less if patients reached 350 Cd4 cells/mm3. Gene expression was evaluated with groups of samples. We demonstrate that this results in shorter and more Illumina microarrays, including 47000 probes by patient and time point. Before accurate lists of differentially expressed genes. Antiretroviral Treatment Interruption (ATI), 8 repeated measures were available ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info C17.5 From single-SNP to wide-locus GWAS: A Computational Biostatistics Approach Identifies Pathways in Small Sample Studies Knut Wittkowski The Rockefeller University, New York, NY, USA Genome Wide Association Studies (GWAS) have had limited success when applied to complex diseases. Analyzing SNPs individually requires several large studies to integrate the often divergent results. In the presence of epistasis between SNPs, intragenic regions, or genes, multivariate approaches based on the linear model (including stepwise logistic regression) often have low sensitivity and generate an abundance of artifacts. Recent advances in distributed and parallel proc-essing spurred methodological advances in nonparametric statistics. GWAS results based on u-statistics for multivariate data (µGWAS) are not confounded by unrealistic assumptions such as linearity or independence and can now also incorporate information about hierarchical data structures. µGWAS draws on two novel concepts. First, utilizing information about the sequence of neighboring SNPs and information from the HapMap project about recombination hotspots. Second, "information content of multivariate data" is used to assess the reliability of data and results. Taken together, this computational biostatistics approach increases power and guards against artifacts, paving the way to comparative effectiveness research and personalized diagnostics. µGWAS typically identifies clusters of genes around biologically relevant pathways and pinpoints functionally relevant regions within these genes. A study of only 185 cases and 370 controls sufficed to integrate previous findings and to generate novel biologically plausible hypotheses about the interplay of genetic risk factors. While most drugs target regulatory processes at the level of the nucleus or cell membrane, µGWAS identified a cluster of genes controlling functional processes in the cytoplasm (cytoskeleton dynamics), suggesting novel indications for drugs currently in clinical tests. I4 Extensions to Epidemiological Designs 53/156 additional information on the cohort. In this context, the estimation of the survival function through the Kaplan-Meier method is based on counts of events and numbers at risk in time, where the contribution of the subject accounts for the sampling probability. This enables to recover the representativeness of the subcohort. Here, we extend this approach to deal with the presence of competing risks. The starting point is the estimator of the crude incidence of a specific cause. This is written in a Kaplan-Meier form, based on count of events due to that cause and counts of subjects at risk, after extending the time to competing events to infinity and applying a suitable weighting to induce independent censoring. The inverse of the sampling probabilities is applied to the number of events of the cause of interest and to the weighted number of subjects at risk. This approach will be applied in the context of a two-phase study on childhood acute lymphoblastic leukaemia, that was planned in order to evaluate the role of genetic polymorphism on treatment failure due to relapse. A subsample was selected for genotyping in a large cohort of patients from a clinical trial. I4.3 A semiparametric approach to secondary analysis of nested casecontrol data Agus Salim Saw Swee Hock School of Public Health, National University of Singapore Many epidemiological studies use nested case-control (NCC) design to reduce cost while maintaining study power. Because NCC sampling was conditional on the primary outcome and matching variables, routine application of logistic regression to analyze secondary outcome will generally produce biased oddsratios. Recently, several methods have been proposed to analyze secondary outcome. These methods are based on either weighted-likelihood or maximumlikelihood. A common feature of all current methods is they require the availability of survival time for the secondary outcome for cohort members not selected into the NCC study. This requirement may not be easily satisfied when the cohort is a hospital cohort where often we only have survival data of those selected into the study. An additional limitation specific to the current maximum-likelihood method is it assumes the hazards of the two outcomes are conditionally independent given covariates. This assumption may not be plausible when individuals have different levels of frailties not captured by the covariates. We provide a maximum-likelihood method that explicitly model the individual frailties and avoid the need to have access to the full cohort data. The likelihood contribution is derived by respecting the original sampling procedure with respect to the primary outcome. The proportional hazard models are used to model the marginal hazards and Clayton’s copula is used to model dependence between outcomes. We show that the proposed method is more efficient than the weighted likelihood method and is unbiased in the presence of frailties. We apply the methodology to study risk factors of diabetes in a cohort of Swedish twins. I4.1 Conditional likelihoods for case-cohort data: Do they exist? Bryan Langholz University of Southern California, Los Angeles, California, USA Since the introduction of the continuous time pseudo-likelihood analysis of case-based sampled cohort data introduced by Prentice 1986, the focus of methodological work on the analysis of case-cohort studies has been on modifications of Prentice's basic approach. The pseudo-likelihood approach has some unappealing features including that there is no clear connection between analysis of case-based binary data and that the variance of the pseudo-score is not the expected pseudo-information, and the method does not easily extend to accommodate more complex sampling designs. While these features are to some degree aesthetic, they are motivation to search for other approaches that are more closely related to likelihood methods for sampled data. Observations that suggest that other, more efficient, approaches C18 Clinical trials III exist will be discussed and possible ways to develop likelihoods based on appropriately specified intensities and sampling theor ywill be presented. C18.1 Dealing with Criticisms and Controversies of Pragmatic Trials I4.2 Lehana Thabane1, Janusz Kaczorowski2, Lisa Dolovich1, Larry Chambers3, on Estimating cumulative incidence adjusting for competing risk using an behalf of CHAP Investigators 1 optimal two-phase stratified design McMaster university, Hamilton, Ontario, Canada, 2Universite de Montreal, Montreal, Ontario, Canada, 3University of Ottawa, Ottawa, Ontario, Canada Paola Rebora, Laura Antolini University of Milan, Bicocca, Italy Pragmatic trials are designed to answer the practical question of whether Research on the role of genetic and biological factors is challenged by the offering an intervention compared with some alternative (eg. usual care) in need of investigating large cohorts with limited resources. In stratified two- routine health care does more good than harm. In this presentation, we will phase designs, a convenient subcohort is sampled and investigated to gain review the similarities and differences between pragmatic and explanatory trials; and common criticisms and controversies of pragmatic trials. We will use 54/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info the CHAP (Cardiovascular Health Awareness Program: www.CHAPprogram.ca) trial-which was a community randomization pragmatic trial designed to assess whether offering a highly organized, community-based CHAP intervention compared to usual care can reduce cardiovascular related outcomes-to illustrate how we addressed some of the criticisms. before evaluation. Consequently, it is of prime interest to determine parameters for describing the learning effect such as the rate of change and the duration of the learning period, as well as the final skill level achieved, in order to adjust for its presence during assessment. In published studies learning curve methodology has provided estimates of the rate of learning and final skill level but not of the time taken to reach it. We demonstrate that the pairwise logistic model incorporates a parameter for estimating the duration of the learning C18.2 period and has easily interpretable parameters. This is achieved by breaking Finding and validating subgroups of enhanced treatment effect in randomized down the model into a branch describing the learning phase and one clinical trials describing cases after final skill level is reached, with the break-point Jeremy Taylor1, Jared Foster1, Stephen Ruberg2 representing the length of learning. This extension is useful as it provides a 1 University of Michigan, Ann Arbor, Michigan, USA, 2Eli Lilly, Indianapolis, measure of the potential cost of learning the intervention and would enable statisticians to discard cases undertaken during the operator's learning phase Indiana, USA and assess the intervention after the optimal skill level is reached. We illustrate We consider the problem of identifying a subgroup of patients who may have the method using data from cardiovascular surgery. an enhanced treatment effect in a randomized clinical trial, and it is desirable that the subgroup be defined by a limited number of covariates. For this problem, the development of a standard, pre-determined strategy may help to C18.5 avoid the well-known dangers of subgroup analysis. We present a method Interim analyses in diagnostic versus treatment studies: differences and developed to find subgroups of enhanced treatment effect. This method similarities involves predicting response probabilities of both potential outcomes for Oke Gerke1, Werner Vach2, Poul Flemming Høilund-Carlsen1 treatment and control for each subject. The difference in these probabilities is 1 Odense University Hospital, 5000 Odense C, Denmark, 2University Medical then used as the outcome in a classification or regression tree, which can Center Freiburg, 79104 Freiburg, Germany potentially include any set of the covariates. We define a measure Q(A) to be the difference between the treatment effect in estimated subgroup A and the Purpose: To contrast interim analyses in paired diagnostic studies of accuracy marginal treatment effect. We present several methods developed to obtain an with interim analyses in (randomized controlled) treatment studies with respect estimate of Q(A), including estimation of Q(A) using estimated probabilities in to differences in planning and conduct. the original data, using estimated probabilities in newly simulated data, cross- Materials and Methods: The term 'treatment study' refers to (randomized) validation-based approaches and a bootstrap-based bias corrected approach. clinical trials aiming to demonstrate superiority or non-inferiority of one Results of a simulation study indicate that the method noticeably outperforms treatment over another and the term 'diagnostic study' to clinical studies logistic regression with forward selection when a true subgroup of enhanced comparing two diagnostic procedures using a third as gold standard. We treatment effect exists. Generally, large sample sizes or strong enhanced compare the design and purpose of interim analyses in treatment and treatment effects are needed for subgroup estimation. Additionally, simulation diagnostic studies and point to some important differences between them using results suggest that the method is fairly insensitive to moderate variations in simulations to exemplify points regarding sample sizes. the true model for the observations. Results: Though interim analyses in paired diagnostic and treatment studies have similarities regarding a priori planning of timing, decision rules, and C18.3 consequences of the analyses, they differ with respect to: (A) sample size Monitoring a Long-Term Efficacy Study for Futility: an Application in adjustments; (B) early decision without early stopping; (C) handling of emerging trends during study conduct. These differences are due to the Huntington's Disease dependence of sample size on the agreement rate between the modalities in David Oakes paired diagnostic studies, the possibility to continue a study despite a clear University of Rochester, Rochester, NY, USA evidence of the superiority of one of the modalities, and the restricted (or even Futility monitoring of efficacy studies with long-term follow-up poses a major impossible) long-term blinding of imaging techniques, respectively. challenge: to enable a useful savings of time and costs, it may be necessary to Conclusion: In diagnostic studies, interim analyses may reveal efficacy early conduct a futllity analysis before the primary outcome data can be obtained for without the need to stop the trial. In addition, they allow sample size the majority of subjects. The challenge, and some possible approaches to adjustments when reliable initial estimates of agreement rates cannot be addressing it, are described in the context of a ongoing study in Huintington's obtained. Finally, by providing common understanding they prevent disease. inappropriate actions as results gradually become known to project team members. C18.4 Assessment of surgical interventions through clinical trials: accounting for the C19 Model selection I impact of learning curves. C19.1 (Student award winner) Olympia Papachristofi, Linda Sharples Advanced Colorectal Neoplasia Risk Stratification by Penalized Logistic MRC Biostatistics Unit,University of Cambridge, Cambridge, UK Regression There has been growing interest in the rigorous assessment of surgical Yunzhi Lin, Menggang Yu, Sijian Wang, Richard Chappell interventions through the conduct of clinical trials. However, for novel University of Wisconsin, Madison, Madison, WI, USA interventions it is expected that the performance of the operators will change over time as experience increases; this learning effect may complicate the Colorectal cancer (CRC) is the second leading cause of death from cancer in evaluation of the intervention by either delaying the start of the trial, or by the United States. To facilitate a tailored screening recommendation, there is a masking aspects of its true impact if surgical performance has not stabilised need for rules that stratify risk for CRC among the 90% of U.S. residents who are considered "average risk". In this article, we investigate into such risk ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info stratification rules for advanced colorectal neoplasia. We use a recently completed large cohort study collected from two clinical programs of screening colonoscopy. Logistic regression models have been used in literature to estimate the risks of CRC based on quantifiable risk factors. However, logistic regression may be prone to overfitting and instability in variable selection. Since most of the risk factors in our study have several categories, it is also tempting to collapse these categories into fewer risk groups. We proposed a L1 penalized logistic regression method that can automatically and simultaneously select variables, group categories, and estimate their coefficients. The model penalizes the L1-norm of both the coefficients and their difference. Thus it encourages sparsity in the categories, i.e. grouping of the categories, and also sparsity in the variables, i.e. variable selection. We apply the penalized logistic regression method to our data. Six variables are selected, with close categories grouped simultaneously, by the penalized regression models. The models are validated with 10-fold cross-validation. The ROC curves of the penalized regression models dominate the ROC curve of naive logistic regression at all cutoff thresholds. C19.2 A universal cross-validation criterion and its asymptotic distribution Daniel Commenges, Cécile Proust-Lima, Benoit Liquet INSERM, Bordeaux, France We consider inference problems where several estimators are available. A general framework is that the estimators are obtained by minimizing an estimating function and they are assessed through another function, that we call the assessment function. The estimating and assessment functions generally estimate risks. A classical case is that both estimate an informatin risk, specifically the cross-entropy. In that case Akaike information criterion (AIC) is relevant. In more general cases, the assessment risk can be estimated by leave-one-out cross-valudation. Since this is computationally demanding, an approximation formula is very useful. A universal approximate crossvalidation criterion (UACV) is given. This criterion can be applied to different estimators including penalized likelihood and maximum a posteriori estimators and different assessment risks such as information risks and continuous rank probability score. The asymptotic distribution of UACV can be derived. An illustration for comparing estimators of a distribution for ordered categorical data derived from threshold models and models based on continuous approximations will be given. C19.3 Modeling continuous predictors with a ‘spike’ at zero: multivariable extensions and handling of related spike variables Carolin Jenkner1, Eva Lorenz2, Heiko Becher2, Willi Sauerbrei1 1 University Medical Center, Institute of Medical Biometry and Medical Informatics, Freiburg, Germany, 2Medical Faculty University of Heidelberg, Epidemiology and Biostatistics Unit, Heidelberg, Germany In epidemiology and clinical research, predictors often have a proportion of individuals with value zero and the distribution of the others is continuous (variables with a spike at zero). Examples in epidemiology are smoking or alcohol consumption and in clinical research laboratory measures, sometimes causes by a lower detection limit of the measurement. Recently, an extension of the fractional polynomial (FP) procedure was proposed to deal with such situations (Royston and Sauerbrei, Royston et. al). To indicate whether a value is zero or not, a binary variable is added to the model. In a two-stage procedure, it is assessed whether the binary variable and/or the continuous FP function for the positive part is required (FP-spike). A study investigating the prognostic effect of hormonal values (estrogen receptor and progesterone receptor) in patients with breast cancer will be used to illustrate the procedure and to compare the results with those of usual FP 55/156 survival models. As there are several correlated prognostic factors, it is important to assess the effects of the individual factors in a multivariable framework. We will present several possibilities of handling a multivariable spike situation. Furthermore, methods for related spike variables are discussed with the aim of creating a comprehensive index for these variables. Royston et al. Stat.Med. 2010; 29: 1219-27. Becher et al. Analysing covariates with spike at zero: a modified FP procedure and conceptual issues [submitted] C19.4 Stability investigations of multivariable regression models derived from low and high dimensional data Willi Sauerbrei1, Anne-Laure Boulesteix2, Harald Binder3 1 Institute of Medical Biometry and Informatics, University Medical Center Freiburg, Freiburg, Germany, 2Department of Medical Informatics, Biometry and Epidemiology, University of Munich, Munich, Germany, 3Institute of Medical Biostatistics, Epidemiology and Informatics, University Medical Center Mainz, Mainz, Germany Multivariable regression models can link a potentially large number of variables to various kinds of outcomes, such as continuous, binary or time-to-event endpoints. Selection of important variables and selection of the functional form for continuous covariates is a key part of building such models but is notoriously difficult due to several reasons. Caused by multicollinearity between predictors and a limited amount of information in the data, (in-)stability can be a serious issue of models selected. For applications with a moderate number of variables, resampling-based techniques have been developed for diagnosing and improving multivariable regression models. Deriving models for high-dimensional molecular data has led to the need for adapting these techniques to settings where the number of variables is much larger than the number of observations. Three studies with a time-to-event outcome, of which one has high-dimensional data, will be used to illustrate several techniques. Investigations at the covariate level and at the predictor level are seen to provide considerable insight into model stability and performance. While some areas are indicated where resampling techniques for model building still need further refinement, our case studies illustrate that these techniques can already be recommended for wider use. Sauerbrei W., Boulesteix A.-L., Binder H. (2011): Stability investigations of multivariable regression models derived for low and high dimensional data. Journal of Biopharmaceutical Statistics, 21:1206-1231. C19.5 Learning Mixtures through merging components Yanzhong Wang1, Mike Titterington2 1 King's College London, London, UK, 2University of Glasgow, Glasgow, UK Among techniques for learning mixtures, the most popular appears to be the EM algorithm. But as a local search algorithm, EM has a number of limitations such as slow to converge, sensitive to initialization, and may get stuck in one of many local maxima of the likelihood function. As an alternative, the IPRA (Iterative Pair-wise Replacement Algorithm), a components merging method capable of fitting mixture models with a large number of components, was proposed by Scott and Szewczyk (2001). The IPRA uses a kernel density estimate as an initial estimate of the large mixture model, and then simplifies the large model by iteratively merging pairs of similar components based on a similarity measure. However, it only applies for one-dimensional data. We extended the IPRA into multidimensional problems by proposing a multivariate IPRA, which uses the minimal spanning tree (MST) to limit searches, thereby reduces the number of comparisons from O(n2) to O(nlogn). With the help of 56/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info the L2E value and information criteria such as AIC and BIC, the best fitting model is then selected from a sequence of resulting mixture models. We compared our multivariate IPRA with the standard EM+BIC approach (MCLUST package in R) on both simulated data and real data from the South London Stroke Register (SLSR) and found that the multivariate IPRA was able to determine the right number of components in the mixture, more robust to outliers, less computational demanding and more efficient on fitting many clusters in large datasets. C20 Prediction in survival analysis C20.1 Sample size planning for survival prediction with focus on high dimensional data Heiko Götte1, Isabella Zwiener2 1 Merck KGaA, Darmstadt, Germany, 2IMBEI, University Mainz, Mainz, Germany examined. The sample consists of 174 patients contributing with 1347 amalgam restorations (censoring percentage of 86.2 \%). We have found a strong inter-cluster predictive ability of both covariate and clustering effects, but a poor intra-cluster predictive ability of the covariate effects. C20.3 Prediction tool for risk of death using history of cancer recurrences in joint frailty models Audrey Mauguen1, Simone Mathoulin-Pelissier2, Virginie Rondeau1 1 INSERM, Centre INSERM U897; Univ Bordeaux, ISPED, Bordeaux, France, 2 Institut Bergonie, Bordeaux, France Evaluating prognostic of patients according to their demographic, biological or disease characteristics is a major issue. It may be used for guiding treatment decisions. In cancer studies, typically, more than one endpoint can be observed before death. Patients may undergo several types of events, such as local recurrences, distant metastases and second cancers, death being considered as terminal event. Accuracy of clinical decisions may be improved when the history of these different events is considered. Thus we want to dynamically assess a patient’s prognosis of death using recurrence information. As previously done in the framework of joint models for longitudinal and time to event data (Proust-Lima and Taylor 2009; Rizopoulos 2011), we propose a dynamic prediction tool based on joint frailty models. Joint modelling accounts for the dependence between recurrent events and death, by the introduction of a random effect shared by the two processes (Rondeau 2007). We aim at producing an accurate estimate of the probability to survive beyond t+w, conditional on information available at the prediction time point t. Prediction is updated with the occurrence of a new event. We will compare the performance of the proposed prediction tool with the performance of a simple prediction model, not taking into account the correlation between intermediate events and death. The proposed tool will be applied on breast cancer data from a French comprehensive cancer centre. Patients with a primary invasive breast cancer and treated with breast-conserving surgery and followed up during more than 10 years will be analyzed. Frequently, researchers try to predict the survival outcome based on high dimensional data. In this case an established procedure is to reduce the number of covariates by applying a variable selection tool and then fitting a model based on the selected variables. Although this model building process is complex, usually no proper study planning is performed and it is often unknown whether the sample size is sufficient to determine a prediction model which leads to an appropriate prediction accuracy when the model is applied to independent validation data. We present formulas for the determination of the training set sample size for survival prediction. Censoring is considered. The sample size is chosen to control the difference between an optimal and an expected prediction error. Prediction is done by Cox models. In the high dimensional setting the sample size has not only an impact on the standard errors of the estimates but also on the number of correctly selected variables. In the case that not all informative variables are included in the final model, the effect estimates are biased towards zero. Omission of informative variables leads to a misspecified Cox model which is known to produce biased estimates. For univariable selection, the magnitude of bias as well as the number of correctly identified variables can be calculated analytically. An C20.4 example illustrates the application of the method. Concordance for prognostic models with competing risks Marcel Wolbers1, Michael T. Koller2, Jacqueline C. M. Witteman3, Thomas A. C20.2 Gerds4 Exploring the discriminatory ability of frailty models 1 Oxford University Clinical Research Unit and Wellcome Trust Major Overseas Robin Van Oirbeek1, Emmanuel Lesaffre1,2 Programme, Ho Chi Minh City, Viet Nam, 2Basel Institute for Clinical 1 2 I-Biostat, KU Leuven, Leuven, Belgium, Department of Biostatistics, Erasmus Epidemiology & Biostatistics, University Hospital Basel, Basel, Switzerland, Medical Center, Rotterdam, The Netherlands 3 Departments of Epidemiology, Erasmus MC – University Medical Center The concordance probability is a very popular tool to measure the predictive Rotterdam, Rotterdam, The Netherlands, 4Department of Biostatistics, ability of a survival model. It typically checks if the ordering of the predicted and University of Copenhagen, Copenhagen, Denmark the observed survival times is the same or concordant for a randomly selected The concordance probability is a widely used measure to assess the pair. While useful, we believe that this measure is too rough and that it may discrimination of prognostic models with binary and survival endpoints. Here, also be of interest to investigate how the concordance probability evolves as we formalize an earlier definition of the concordance probability for predicting the difference in survival time of the randomly selected pair increases. We an event of interest based on a regression model with a competing risks therefore propose a concordance measure that measures both ordering and endpoint (Wolbers et al, Epidemiology 2009;20: 555-561). We illustrate the distance in survival time for a randomly selected pair. Moreover, three specific properties of the concordance probability for varying effects of a single numeric adaptations of the concordance probability were proposed for the proportional covariate on the cause-specific hazards of both the event of interest and the hazard frailty model (Van Oirbeek and Lesaffre, 2010). In this presentation, we competing event, respectively. In this setting, the concordance probability for a generalize these 3 adaptations to every type of frailty model and we develop a covariate which is positively associated with the hazard of the event of interest procedure to calculate a credible/confidence interval within the decreases if the covariate is also positively associated with the hazard of the Bayesian/likelihood framework, as well as an internal validation and an outlier competing event whereas concordance increases if the association with the detection scheme. The properties of all these developments are investigated in competing event is negative. an extensive simulation study and is illustrated on a real clinical study. In this study, the effect of different treatment modalities as well as the effect of patient, For right censored data, we investigate inverse probability of censoring tooth and operator characteristics on the longevity of amalgam restorations is weighted (IPCW) estimates of a truncated concordance index. The estimates are based on a working model for the censoring distribution and the simplest ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info model assumes that the censoring distribution is independent of the predictor variables. We show that if the working model is correctly specified then the IPCW estimate consistently estimates the concordance probability. The small sample properties of the estimates are assessed in a simulation study. We further illustrate the methods by computing the concordance probability for a prognostic model of coronary heart disease (CHD) in the presence of the competing risk of non-CHD death. C20.5 Comparing areas under time-dependent ROC curves under competing risk Paul Blanche, Hélène Jacqmin-Gadda Univ. Bordeaux, ISPED, INSERM U897, Bordeaux, France For many diseases, it is relevant to evaluate and compare the ability of biomarkers to predict the onset of the disease. To do so, estimators of the area under the time-dependent ROC curve (AUC) have been developed accounting for censored data due to lost of follow-up (Heagerty et al, 2000; Uno et al, 2007). Under the competing risk setting, two definitions of ROC curve and AUC can be considered according to how one defines a case and a control (Zheng et al, 2011). Accounting for censoring, we propose simple Inverse Probability of Censoring Weighting (IPCW) estimators for both AUC definitions. We establish large sample theory of these estimators and derive a test statistic to compare the AUCs of two markers, even when markers are measured on the same subjects. Two weightings are considered: one based on Kaplan-Meier estimator leading to a fully non-parametric testing procedure and another based on a semi-parametric Cox model to deal with marker-dependent censoring. A simulation study highlights the finite sample behaviour of the test. We apply the methodology to compare several psychometric tests for predicting dementia in the elderly, accounting for death competing risk. Data come from the French PAQUID cohort including 3777 subjects aged 65 years and older at baseline. C21 Statistical methodology I C21.1 High dimensional regression using decomposition-gradient-nuisance method and its application in epidemiological case control studies Yuanzhang Li, Tianqing Liu, David Niebuhr Walter Reed Army Institute of Research, Silver Spring, MD, USA Regression of high dimensional data is difficult when the sample size is small. The traditional ordinary least squares estimation performs poorly in this situation. Even though the sample size is not small, the high correlation among some of the high dimensional predictors cannot be avoided. Multiple linear regressions are very sensitive to predictors being in near-collinearity. When this happens, the model parameters become unstable with large variance. Those phenomenons often occur in epidemiological studies, especially for the analyses involving high dimensional predictors. We propose a combined linear approach which uses a space-decomposition method to reduce the collinearity, and then a gradient-nuisance method to select "better" predictors as well as nuisance factors to control for the variance heterogeneity. We apply the proposed method to a military schizophrenia data including 294 cases and 344 matched controls with 48 biomarkers and seven antibody agents. We identify a small sub group of biomarkers and antibody agents which provide important insights on schizophrenia. Using a longitudinal general linear regression on the combinations of those selected biomarkers and agents, numerical results demonstrate that the proposed approach can significantly improve predictive efficiency with a substantial dimension reduction. Simulation based randomly split data sets shows the selection is robust and stable. 57/156 C21.2 Choice of the Berger and Boos confidence coefficient in an unconditional test for equality of two binomial probabilities Stian Lydersen, Mette Langaas, Øyvind Bakke Norwegian University of Science and Technology, Trondheim, Norway Exact unconditional tests for comparing two binomial probabilities are generally more powerful than conditional tests like Fisher’s exact test. Such tests can be further improved by the Berger and Boos confidence interval method, where a p-value is found by restricting the common binomial probability under H 0 to a 1γ confidence interval. Different default values of γ, such as 10-3, 10-4, and 10-6, have been used in software. We studied average test power for the exact unconditional z-pooled test for a wide range of cases with balanced and unbalanced sample sizes, and significance levels 0.05 and 0.01. Among the values 10-3, 10-4, …, 10-10, the value γ = 10-4 was optimal or approximately optimal in all the cases we looked at, and can be given as a general recommendation. Reference Lydersen, S., Langaas, M., & Bakke, Ø. The Exact Unconditional z-pooled Test for Equality of Two Binomial Probabilities: Optimal Choice of the Berger and Boos Confidence Coefficient. Journal of Statistical Computation and Simulation. Available online: 04 Jul 2011. C21.3 Surrogate endpoints in breast and colon cancer: An evaluation of validation studies Christoph Schürmann, Ralf Bender, Thomas Kaiser, Elke Vervölgyi, Volker Vervölgyi, Beate Wieseler Institute for for Quality and Efficiency in Health Care (IQWiG), Cologne, Germany In oncology, endpoints like disease free survival or progression free survival are frequently used as surrogates for overall survival. Yet, validation is necessary to infer meaningful conclusions from the surrogate to the actual endpoint. We present the results of a systematic review in which we searched the literature for validation studies in breast and colon cancer. We developed and assessed a set of reliability criteria for included studies, i. e. if studies applied a suitable method of validation, if there was some analysis confirming robustness of the results, if the data base used was complete, if the analysis was specific with respect to indication and intervention, and if there was a consistent definition of endpoints. Apart from these qualitative aspects, we analysed quantitatively how close the relationship of the surrogate endpoints with the endpoints of interest was in terms of trial-level correlation. We found 6 validation studies in breast cancer and 15 studies in colon cancer. In each case, we considered the results not to be reliable, because always at least two of the given aspects remained unclear or were not satisfactorily fulfilled. Besides, few of the studies gave evidence of strong correlation. We propose how to draw general conclusions about the validity based on the reliability aspects and the size of the correlation. Our analysis shows that, in principle, validation studies could address our concerns about reliability. But unless this is properly considered, the validity of surrogate endpoints is not clearly proven. 58/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info C21.4 Massey University, Palmerston North, New Zealand Use of surrogate endpoints for improving efficiency, reduction of sample size Twin studies provide naturally matched pairs that can exploit within-pair and modification of Mantel-Haenszel estimator for odds ratio comparisons of data to avoid confounding exposure-outcome associations by shared factors. For binary outcomes, paired data can be analysed using the Buddhananda Banerjee, Atanu Biswas logistic regression model of Neuhaus and Kalbfleisch (1998) with a linear Indian Statistical Institute, Kolkata -- 700 108, India predictor that includes terms for both the mean and difference in exposure with Surrogate end-points are used when the true end-points are costly or time- between- and within-pair regression coefficients B and W respectively. When consuming. In a typical set up we observe a fixed proportion of true-and- estimates of B and W differ the former may not provide useful information surrogate responses, and the remaining proportion are only-surrogate about the latter. responses. It is obvious that the inclusion of such only-surrogate end-points If B = W one scenario where the issue of differing estimates of B and W has a increase the efficiency of associated estimation. In this present paper we want straightforward resolution is when the pair exposure mean is measured with to quantify the gain in efficiency as a function of the proportion of available true error, but that the within-twin difference is subject to negligible error. For responses. Also we obtain the expression of the gain in true sample size at the instance, siblings reporting nutritional or alcohol intake may be accurate in expense of surrogates to achieve a fixed power, as a function of the proportion comparison to each other, but less good on an absolute scale. Failure to of true responses. We present our discussion in the two-treatment set up in the account for the measurement errors leads to attenuation in the estimates of B, context of odds ratio. We illustrate the procedure using some real data set. generating an apparent discrepancy with W. By using the SIMEX method of Cook and Stefanski (1994) with shared within-pair measurement error, it is possible to adjust for this and generate an estimate of B that is considerably C21.5 more efficient than estimating B alone or using conditional logistic regression. Estimation of between- and within-pair regression effects in logistic regression We examine the efficacy of this approach through a simulation study, and an with shared measurement error application exploring the association between low birthweight and cord blood 1 2 3 Lyle Gurrin , Elizabeth Williamson , Martin Hazelton erythropoietin as a marker of hypoxic stress in utero and possible growth 1 Melbourne School of Population Health, Melbourne, Victoria, Australia, restriction (Carlin, Gurrin and Sterne (2005)). 2 Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia, 3Institute of Fundamental Sciences - Statistics, ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info 59/156 Wednesday, 22 August Morning sessions (IP3, I5 , C22 – C25) registered patient-specific data, I5 Causal inference we ask what outcome difference is to be expected when patients are treated in center A rather than B. Following the causal question: what difference does center A make to the patients' outcome, we evaluate the center's impact on its own typical patient mix. Statistical approaches under either assumption `no unmeasured confounders' and `instrumental variables' encounter serious challenges with many imbalanced and sometimes small centers. We consider what can be achieved by (doubly robust) (structural) regression models and how penalization is best exploited. In performing center evaluations we aim to strike a balance between type I and type II errors as appropriate for the study goal. Because Belgians focus on confidential feedback to provide the center with a tool for self correction, we view this as an early warning system and place greater emphasis on the type II error. After characterizing the center's global performance, we are concerned with direct and indirect effects and wonder, for instance, to what extent a lack of imaging facilities has contributed to a center?s weaker performance. I5.1 Causal mediation analysis with applications to perinatal epidemiology Tyler VanderWeele Department of Epidemiology, Department of Biostatistics, Harvard School of Public Health, Harvard University, USA Mediation analysis concerns assessing the mechanisms and pathways by which causal effects operate. Statistical techniques to address these questions have been used in the social science and epidemiologic literature for some time. More recently these techniques have come under critique for inadequately dealing with issues of confounding and causal interpretation. The talk will focus on the relationship between traditional methods for mediation and those that have been developing within the causal inference literature. For dichotomous and continuous outcomes, we discuss when the standard approaches to mediation analysis employed in epidemiology and the social sciences are valid. Using ideas from causal inference and natural direct and indirect effects, we provide alternative mediation analysis techniques when the standard approaches will not work. We discuss the no-confounding C22 Survival analysis II assumptions needed for these and sensitivity analysis techniques when those assumptions fail. Further discussion is given as to how such mediation analysis C22.1 approaches can be extended to settings in which data come from a case- Properties of net survival estimation control study design. The methods are illustrated by various applications to Maja Pohar Perme perinatal epidemiology. Department of Biostatistics and Medical Informatics, University of Ljubljana, Ljubljana, Slovenia I5.2 The survival analysis of long term studies is often interested in the mortality Estimation and extrapolation of treatment effects due to the disease in question but faced with the problem of many deaths Andrea Rotnitzky1,2, James Robins3, Liliana Orellana4 occurring due to other causes. Furthermore, the cause of death is often 1 Department of Economics, Di Tella University, Buenos Aires, Argentina, 2 unknown or unreliable. A solution to this problem is to assume that hazard due Department of Biostatistics, Harvard School of Public Health, Harvard to other causes can be described by the general population mortality. The University, USA, 3Department of Epidemiology, Harvard School of Public methodology based on this assumption is referred to as relative survival, its Health, Harvard University, USA, 4FCEyN, Universidad de Buenos Aires, most important field of usage is cancer registry data. One of the basic aims of the analysis of cancer registry data is to estimate quantities which are Buenos Aires, Argentina comparable between different countries or time periods and thus not affected In this talk I discuss methods for using the data obtained from an observational by the differences in other cause mortality. We have recently shown that the database in one health care system to determine the optimal treatment regime methods in standard use provide biased estimates and proposed a new for biologically similar subjects in a second health care system when, for measure of net survival that satisfies this aim. In this work, we study its cultural, logistical, and financial reasons, the two health care systems differ properties and behaviour in practice and discuss its assumptions and (and will continue to differ) in the frequency of, and reasons for, both laboratory interpretation. tests and physician visits. I also describe methods for estimating the optimal The results are illustrated using Slovene cancer registry data. timing of expensive and/or painful diagnostic or prognostic tests. Diagnostic or prognostic tests are only useful in so far as they help a physician to determine the optimal dosing strategy, by providing information on both the current health C22.2 state and the prognosis of a patient because, in contrast to drug therapies, Estimating the loss in expectation of life due to cancer using flexible parametric these tests have no direct causal effect on disease progression. The proposed survival models methods explicitly incorporate this no direct effect restriction. Therese Andersson1, Paul Dickman1, Sandra Eloranta1, Paul Lambert1,2 1 Karolinska Institutet, Stockholm, Sweden, 2Leicester University, Leicester, UK I5.3 A useful summary measure for survival data is the expectation of life, which Protecting against errors: causal effect estimates for the evaluation of can be calculated by obtaining the area under a survival curve. The loss in the quality of care over many (cancer) centers expectation of life is the difference between the expectation of life in the Els Goetghebeur, Jozefien Buyze, Machteld Varewyck, Stijn Vansteelandt general population and the expectation of life in a diseased population. This Department of Applied Mathematics and Computer Science, Ghent University, measure is used very little in practice as its estimation generally requires extrapolation of both the expected and observed survival. Belgium To evaluate quality of (rectal) cancer care over Belgian centers based on The extrapolation of the expected survival is fairly straight-forward, but 60/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info assumptions have to be made for the observed survival. A parametric distribution can be used, but it is difficult to find a distribution that captures the underlying shape of the survival function. Extrapolation using relative survival is more stable and reliable. Relative survival is defined as the observed survival divided by the expected survival, and the mortality analogue is excess mortality. Hakama and Hakulinen showed how extrapolation of relative survival can be done for life-table data, by assuming that the excess mortality has reached zero (statistical cure) or has stabilized to a constant. By instead using a flexible parametric approach, introduced by Royston and Parmar, for estimating the excess mortality we can estimate the loss in expectation of life for individual level data. We have evaluated the extrapolation from flexible parametric models, and the results agree very well with observed data. We have developed user friendly software to enable estimation of the loss in expectation of life. Results will be presented for a variety of cancer sites. the approximation is excellent when the data set has at least 10-20 events for each effective degree of freedom (edf) of the model, where the edf is computed in a penalized regression sense. In models with a random effect per subject the edf can easily exceed the number of events and a solution based on the Laplace will seriously underestimate the MLE variance. This can occur for any data set where a non-neglible fraction of the random effects coefficients b correspond to subgroups with no events. Interestingly, this is exactly the case where the MLE is biased large. When the approximation is wrong, it may be better than the truth. We will try to give some insight into how this occurs along with practical guideance for users. C22.3 Sample Size Calculation and Re-estimation for Recurrent Event Data Katharina Ingel, Antje Jahn-Eimermacher Institute for Medical Biostatistics, Epidemiology and Informatics, University Medical Center of the Johannes Gutenberg-University, Mainz, Germany Simulations usually accompany proposals, or comparisons, of measures of explained variation (other names may be used) in statistical literature. Simulations are supposed to illustrate, or often even ‘prove', certain claims about the measures, or their relative merits. Important aspect of such simulations is censoring, other aspects, usually less important, are distributions of covariates, number of covariates, and effects of covariates. And a standard, almost universal, approach to censoring is such that life times are generated using exponential distribution, and censoring times are generated using a uniform distribution. Thus, by changing the supporting interval of the censoring distribution, different proportions of censoring are obtained. We argue that such a setting is more or less useless for studying properties of measures of explained variation. The reason is simple: under such setting, we can never see life times greater than the upper value of the censoring interval, so that studying consistency or bias, unless limited to that value, is impossible. And from this quite some misunderstanding of the properties of the measures follows. Some clinical trials compare the repeated occurrence of the same type of event, e.g. epileptic seizures, between two or more treatment groups. The Andersen-Gill model has been proposed to analyse recurrent event data (Andersen and Gill, The Annals of Statistics, 1982). For sample size calculation Bernardo and Harrington (Statistics in Medicine, 2001) suggest a formula, which relies on the independence of inter-event-times within individuals conditional on the covariate values. This assumption is violated if individuals are heterogeneous in their baseline hazard. To control the type I error in these situations, a robust variance estimate is required for calculating the test statistic, which will decrease the actual power of the trial. We propose an adjusted sample size formula to achieve the desired power even in the presence of patient heterogeneity in baseline hazard. Adjustment is performed by the use of a nuisance parameter which accounts for the heterogeneity and is derived from characteristics of the robust variance estimate (Al-Khalidi et al, Biometrics, 2011). In the planning phase of a trial there will usually be some uncertainty about the nuisance parameter. We explore how blinded or unblinded internal pilot data can be used to estimate the nuisance parameter and to adjust the sample size based on that estimate. The performance of this internal sample size reestimation design with respect to type I error and power is evaluated through simulations. We illustrate our results with clinical data on the repeated occurrence of epileptic seizures. C22.4 Mixed Effects Cox Models and the Laplace Transform Terry Therneau Mayo Clinic, Rochester, Minnesota, USA Random effects Cox models can be divided into two broad classes: special cases where the algebra can be worked out explicitly and general programs that allow a range of models. The latter assume a Gaussian distribution for the random effects because of it's flexibility, but that leaves an intractable integral. The Cox partial likelihood, however, is normally very quadratic: the NewtonRaphson converges in 2-4 steps without recourse to alternate starting points, step halving, or other sophistication. This makes the Laplace transform attractive, which replaces the integrand with a quadratic approximation, and this is the computational approach of most software packages. We find that C22.5 On using simulations to study explained variation in survival analysis Janez Stare1, Nataša Kejžar1, Delphine Maucort-Boulch2 1 University of Ljubljana, Ljubljana, Slovenia, 2University of Lyon, Lyon, France C23 Measurement error C23.1 (Scientist award winner) Regression toward the mean and ANCOVA in observational studies Péter Vargha Semmelweis University, Budapest, Hungary ANCOVA can produce unbiased estimate of group differences in observational studies as well if either covariate distribution in the groups can be regarded identical, or covariate can be assumed as free of error term, i.e., there is no regression toward the mean (RTM). However, in case of different baseline distribution RTM results inevitably in biased estimation of group effect. Three cases are discussed. In the first one the goal is group comparison of changes, where using ANCOVA with baseline value as covariate is a possible alternative of t-test. Lord’s paradox is presented with critical evaluation of Holland and Rubin’s corresponding results. Their explanation of the paradox uses a term of spontaneous change, not included in the original example. Their results can one (as some do) mistakenly interpret as proving that the choice between t-test and ANCOVA in general simply depends on untestable assumption on spontaneous changes. An example of the second case, where comparing means of ratio of variables and ANCOVA are the alternatives, was published by Sir Ronald Fisher himself. The two approaches resulted in different conclusions, too. In the general case of group comparison with a covariate there is no simple alternative to ANCOVA. To evade bias an assumption has to be made on the share of the covariate in the scatter arround regression lines. ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info C23.2 A multilevel misclassification model for spatially correlated binary data: An application in oral health research Timothy Mutsvari1, Dipankar Bandyopadhyay2, Dominique Declerck3, Emmanuel Lesaffre1,4 1 L-BioStat, Katholieke Universiteit Leuven and Hasselt Universiteit, Leuven, Belgium, 2Division of Biostatistics, School of Public Health, University of Minnesota, Minnesota, USA, 3Department of Oral Health Sciences, Katholieke Universiteit Leuven, Leuven, Belgium, 4Department of Biostatistics, Erasmus Medical Center, Rotterdam, The Netherlands 61/156 C23.4 Variable Selection by Lasso in Regression with Measurement Error Øystein Sørensen, Arnoldo Frigessi, Magne Thoresen Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway Regression with the Lasso penalty is widely used for variable selection in highdimensional (p>>n) statistical problems. When covariates relevant to the outcome and covariates irrelevant to the outcome are correlated, Lasso can select false positives and discard true positives. The Strong Irrepresentable Condition (SIC) is an upper bound for such correlations, where the bound depends on the sparsity of true non-zero coefficients. When data satisfy the SIC, the probability of correct model selection is bounded from below by a probability tending to 1 asymptotically, under suitable conditions. In this paper we investigate the effect of measurement error on the variable selection performance of the Lasso. This is highly relevant, e.g., in the context of microarray experiments, where the presence of measurement error is well documented, and the Lasso is a popular option. We introduce the SIC with Measurement Error (SIC-ME). Furthermore, we prove that SIC-ME implies a lower bound on the probability for Lasso with error-prone covariates to select the true model. Next, we ask: If the true error-free data come from a distribution whose covariance matrix satisfies SIC, which constraints must the distribution of measurement errors satisfy for the SIC-ME also to hold? The answer is illustrated for some commonly assumed covariance structures, and constraints on the measurement errors are derived. Finally, we demonstrate through simulations that when the distribution of the error-prone covariates satisfy the SIC-ME relatively far from its upper limit, using Lasso with covariates from this distribution gives a good chance of recovering the true model. Dental caries is a highly prevalent disease affecting the tooth's hard tissues by acid-forming bacteria. The past and present caries status of a tooth is characterized by a response called caries experience (CE). Several epidemiological studies have explored risk factors for CE. However, the detection of CE is prone to misclassification which needs to be incorporated into the epidemiological models on CE. From a dentist's point of view, it is most appealing to analyze CE on the tooth's surface, implying that the multilevel structure of the data (surface-tooth-mouth) needs to be taken into account. In addition, CE data are spatially correlated, i.e. a carious surface may influence the decay process of the neighboring surfaces. While in the literature it is assumed that misclassifications occur in an independent manner, the nature of scoring CE rather suggests that the misclassification process has a spatial structure. To examine this hypothesis we developed a Bayesian multilevel logistic regression model with a spatial association structure via a conditional autoregressive (CAR) prior distribution. This model assumes dependent misclassification and aims to have better insight in how CE is scored. The model is applied to validation data of the well-known Signal Tandmobiel study, whereby 148 children were examined by a benchmark scorer and 16 dental examiners. Our results indicate a substantial spatial dependency in (wrongly) scoring CE, that there is also considerable clustering and that some covariates affect the scoring behavior, i.e. dentition type, tooth type and position of the tooth in the mouth. C23.5 Adjustment for genotyping measurement error in a case-control study C23.3 Matthew Cooper1, Elizabeth Milne1, Kathryn Greenop1, Sarra Jamieson1, Projecting error: Understanding measurement error in principal components Denise Anderson1, Frank van Bockxmeer1, Bruce Armstrong2, Nicholas de Klerk1 Kristoffer Herland Hellton, Magne Thoresen 1 University of Western Australia, Perth, Australia, 2University of Sydney, University of Oslo, Oslo, Norway Sydney, Australia Principal component analysis (PCA) is one of the most widely used dimension Background reduction techniques, especially for high-dimensional data such as microarray Genotyping has become more cost-effective and less invasive with the use of expression data. With PCA it is possible to visualize the genetic information buccal cell sampling. However, low or fragmented DNA yields from buccal cells and obtain a basis for classification and clustering. The components are based sometimes requires additional whole genome amplification to produce on different loadings of the variables, which are used to reparametrise the data sufficient DNA for typing. In our case-control study, discordance was found as component scores. The loadings are thought to represent the weight of between genotypes derived from blood and whole genome amplified buccal each gene in a physiological process explaining the gene variability. DNA samples. However, there is inherent noise in microarray expressions, which underline Aims the importance of understanding the effect of measurement error on the To develop a user-friendly method to correct for this genotype misclassification, loadings and scores. When representing the noise by an additive model, we as existing methods were not suitable for our purposes. can characterise the bias caused by measurement error. We propagate the distributional assumption on the errors through the principal component Methods analysis, using analytical expression obtain by perturbing the eigenvalue Discordance between the results of blood and buccal-derived DNA was able to be assessed, but only in disease cases, some of whom had both blood and problem. buccal samples. Using the misclassification matrices as probability Finally, we use the resulting statistical properties to discuss and interpret the distributions, we sampled likely values for corrected genotypes for controls with effects of measurement error on loadings and scores. It is also possible to find only buccal samples, creating multiple datasets for analysis. Each dataset was the effects on different dimension reduction criterions, such as the Kaiser rule analysed separately, adjusting for multiple covariates using logistic regression. and Scree plot. Especially the role of the projected error, which represents the Regression coefficients were then combined using standard methods for relationship between the data and the error structure, will be important in multiple imputed datasets. identifying situations where measurement error will have little impact. Results Application to synthetic datasets was effective in producing correct odds ratios 62/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info (ORs) from data with known misclassification. Moreover, when applied to each of six bi-allelic loci, correction altered the ORs in the expected way given the type of misclassification Shown. Increasing the size of the misclassification data set increased the precision of the effect estimates. Conclusions Bias arising from differential genotype misclassification can be reduced by correcting results using this method whenever data on concordance of genotyping results with those from a different and probably better DNA source are available. and to obtain an appropriate model intercept when applying the model in a new population. We evaluate the consistency of model performance using the existing internal-external cross-validation approach, using an empirical Deep Vein Thrombosis dataset as an example. We found that the resulting prediction models are most generalizable when predictor effects are homogeneous. This can be achieved by excluding heterogeneous variables from the model or by including additional variables that explain heterogeneity of predictor effects. When baseline risks are heterogeneous, stratified estimation of the model intercept appears to be the best approach. An appropriate model intercept can then be derived from the outcome proportion in the population of interest, yielding superior calibration. C24 Prediction The approaches we propose can be used to develop a single, integrated C24.1 prediction model from multiple IPDs that has superior generalizability. With Relative ROC curves: a novel approach for evaluating the accuracy of a minimal demographic information, the resulting model can be calibrated to new populations. marker to predict the cause-specific mortality Marine Lorent1, Magali Giral2, Yohann Foucher1 1 Department of Biostatistics EA 4275, Clinical Research and Subjective Measures in Health Sciences, Nantes University, Nantes, France, 2 Transplantation, Urology and Nephrology Institute (ITUN), Nantes Hospital and University, Inserm U1064, Nantes, France C24.3 Using Machine learning methods for event related potential (ERP) brain activity analysis Daniel Stahl Determining prognostic markers of mortality for patients with chronic disease is King's College London, London, UK important for identifying high-risk subjects for death and optimizing medical management. The usual approach for this purpose is the use of time- Machine learning and other computer intensive pattern recognition methods dependant ROC curves which are adapted for censored data. Nevertheless, an are successfully applied to a variety of fields that deal with high-dimensional important part of the mortality may be not due to the chronic disease and it is data and often small sample sizes such as genetic microarray of fMRI data. often impossible to individually identify whether or not the deaths are related to The aim of this presentation is to assess the usefulness of machine learning the disease. In survival regression, a solution is to distinguish between the methods for the analysis of event-related potential (ERP) data. Event-related expected mortality of a general population (estimated on the basis of mortality brain potentials (ERPs) are a non-invasive method of measuring brain activity tables) and the excess of mortality related to the disease, by using an “additive during cognitive processing with high temporal resolution. The analysis of averaged ERP measurements usually involves large number of univariate relative survival model”. mean group comparisons, such as comparing responses at different We propose a new estimator of time-dependant ROC curve that includes electrodes, between a clinical and a control group. This approach typically relative survival concept in order to evaluate the capacity of a marker to predict results in a multiple testing problem. Machine learning methods allow the the disease-specific mortality. analysis of datasets with a large number of variables relative to sample size. We illustrate the utility of such relative ROC curves by two different Cross-validation methods to assess the predictive performance of a derived applications: 1) predicting the mortality related to kidney transplant in end- model, thereby avoiding multiple testing problems. The usefulness of two stage renal disease patients and 2) predicting the mortality due to primary methods, regularized discriminant function analyses and support vector biliary cirrhosis (PBC) in patients with diffuse large cell lymphoma (DLCL). In machines, will be demonstrated by reanalysing an ERP dataset from infants these applications, the capacities of prediction of scoring already established (Elsabbagh et al., 2009). Using cross-validation, both methods successfully are evaluated. discriminated above chance between groups of infants at high and low risk of a The results demonstrate the interest of the proposed estimator of relative ROC later diagnosis of autism. curves. C24.4 C24.2 How much data are required to develop a reliable risk model? A framework for developing and implementing clinical prediction models across Khadijeh Taiyari1, Gareth Ambler2, Rumana Z. Omar3 multiple studies with binary outcomes 1 Department of Statistical Science, University College London, London, UK, 2 Thomas Debray1, Karel Moons1, Hendrik Koffijberg2, Richard Riley2 Department of Statistical Science, University College London, & UCL 1 2 Research Support Centre, London, UK, 3Department of Statistical Science, UMC Utrecht, Utrecht, The Netherlands, University of Birmingham, University College London, & UCL Research Support Centre, London, UK Birmingham, UK The availability of participant-level data from multiple sources is an increasingly prevalent phenomenon in prediction research. However, the corresponding populations typically differ in important aspects, such as baseline risk. This has driven the adoption of meta-analytical approaches for appropriately dealing with heterogeneity when combining such data. Unfortunately, these metaanalytical approaches do not provide a single prediction model that can readily be applied to new populations. Instead, they reveal the variability of baseline risk and predictor effects across studies, and provide little guidance about how then to proceed in integrating these findings. We propose several approaches to account for heterogeneity in baseline risk The ‘rule of 10’ is often used to determine how many predictors should be considered for inclusion in a risk prediction model. This rule suggests that the number of parameters in the model should not exceed the number of events in the dataset divided by ten. This was originally proposed to ensure the unbiased estimation of regression coefficients with confidence intervals that have correct coverage. However, little research has been done to assess the adequacy of the rule regarding predictive performance. This study evaluates this rule using simulation based on real cardiac surgery datasets. Different scenarios were investigated by changing the number of events, the outcome prevalence, the number of noise variables and the amount of ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info 63/156 prognostic information. Logistic regression models were fitted to simulation data and validated using measures that assess calibration, predictive accuracy and discrimination. The results suggest that model calibration deteriorates with decreasing events, increasing outcome prevalence and decreasing prognostic information. Additionally, calibration can be poor even when there are 10 events per variable (EPV). This problem can often be fixed by applying post-estimation shrinkage, though this may not work if there is little prognostic information. Discrimination deteriorates with decreasing events in the models with low prognostic information. Strictly adhering to the “Rule of 10” may produce poorly calibrated risk models and, scholars need to consider the outcome prevalence when designing studies to develop risk prediction models. the analysis involves an interaction. We describe when JAV gives consistent estimation, explore by simulation the bias of all three methods, and illustrate methods using the EPIC study. JAV gives consistent estimation when the analysis is linear regression with quadratic or interaction term and X is missing completely at random. JAV may be biased when X is missing at random, but this bias is generally less than for passive imputation and PMM. Coverage for JAV was usually good when bias was small. However, in some scenarios with a more pronounced quadratic effect, bias was large and coverage poor. When the analysis was logistic regression, JAV's performance was sometimes very poor. PMM generally improved on passive imputation, in terms of bias and coverage, but did not eliminate the bias. C24.5 C25.2 Comparison of multiple imputation methods for repeated measurements studies Oya Kalaycioglu, Andrew Copas, Rumana Omar University College London, Department of Statistical Science, London, UK Dynamic Predictions of Repeated Events of Different Types by Landmarking Z.J. Musoro, R.B. Geskus, A.H. Zwinderman Academic Medical Center, Amsterdam, The Netherlands The landmarking paradigm offers a flexible way to characterize the association between a longitudinal biomarker process and the time until a clinical event. By facilitating direct prediction of survival probabilities in the presence of timedependent covariates and time-variant coefficients, landmark models present an alternative to full probability joint models. We studied post kidney transplantation records of 467 patients (at the Academic Medical Center, Amsterdam) who had repeated events of different types, and were repeatedly measured for multiple biomarkers. Landmark points were defined at the 20th to 80th percentile of unique infection times (years). Infection-specific Cox proportional hazards models with landmark-dependent frailties were considered. The baseline hazard was allowed to vary by landmark. Dependency of the infection specific hazards on the biomarker history was assumed to be via current biomarker values at the landmark points only. Patients’ baseline covariates (age, gender, type of immune suppressive treatment, and duration of dialysis prior to transplant) were allowed to have landmark-dependent coefficients. We adopted options to smooth the coefficients explained over the landmarks. Models assuming landmarkinvariant baseline hazard and coefficients were also evaluated. Our findings revealed that the prognostic effect of natural killer cells on both viral and upper respiratory infections was fairly constant over landmarks (approximated hazard ratio of -1.7 and -1.5 respectively), while the effect of CD3+ cells on upper respiratory infection was slightly larger for early landmark points (hazard ratio of -1.4 versus -0.75). Also, the effect of CD3+ cells on viral infections seemed to increase linearly with landmark numbers. C25 Multiple imputation methods Missing data is a common problem in medical research. Various statistical methods exist to handle missingness, however limited work has been done for developing imputation methods for longitudinal studies. The aim of this study is to evaluate existing methods that have been proposed to impute missing data for repeated measurements studies via simulations based on real data with 50% observations missing. These include: (1) Random intercept extension of multiple imputation using multivariate normal imputation model (MVNI), (2) Multiple imputation by chained equations (ICE) (3) Random intercept extension of ICE, (4) Bayesian multiple imputation with univariate hierarchical imputation models. Comparisons were made with the likelihood analysis of all available cases. AC analysis is unbiased after adjusting for predictors of missingness in the analyses when missingness is at random. The main gain in using MI is the efficiency, especially if missingness is non-monotone. Amongst the imputation methods, MVNI provided unbiased estimates for the regression coefficients of continuous variables, even if these variables showed departures from normality. However, imputing incomplete binary variables assuming multivariate normality resulted in some bias. ICE approaches do not guarantee unbiased estimates for incomplete non-normal continuous variables and transformations did not help. Bayesian imputation using hierarchical Gamma and Half-Normal imputation models for skewed variables reduced bias and improved efficiency. For incomplete variables satisfying multivariate normality, MVNI and ICE provided similar and unbiased results. Multiple imputation methods for repeated measurements data should be used after careful investigation of the distribution of the incomplete variables, assumptions regarding correlations and missingness pattern. C25.1 C25.3 Multiple Imputation of Missing Covariates with Non-Linear Effects and Confidence intervals after multiple imputation: combining profile likelihood Interactions: an Evaluation of Statistical Methods information from logistic regressions Shaun Seaman1, Jonathan Bartlett2, Ian White1 Georg Heinze1, Meinhard Ploner2, Jan Beyea3 1 2 MRC Biostatistics Unit, Cambridge, UK, London School of Hygiene and 1Medical University of Vienna, Vienna, Austria, 2data-ploner.com, Brunico, Italy, 3 Tropical Medicine, London, UK CIPI Consulting in the Public Interest, Lambertville, NJ, USA Multiple imputation is often used for missing data. When a model contains as In the logistic regression analysis of a small-sized, case-control study on covariates more than one function of a variable, it is not obvious how to impute Alzheimer's disease, some of the risk factors exhibited missing values, missing values. Consider regression with outcome Y and covariates X and X 2 motivating the use of multiple imputation. Usually, Rubin's rules (RR) for In `passive imputation' a value X* is imputed for X and then X 2 is imputed as combining point estimates and variances would then be used to estimate (X*)2. A recent proposal is to treat X 2 as `just another variable' (JAV) and (symmetric) confidence intervals (CI), on the assumption that the regression impute X and X2 under multivariate normality. coefficients were distributed normally. Yet, rarely is this assumption tested, with We investigate three methods: 1) linear regression of X on Y to impute X and or without transformation. In analyses of small, sparse, or nearly separated passive imputation of X2; 2) same regression but with predictive mean data sets, such symmetric CI may not be reliable. Thus, RR alternatives have matching (PMM); and 3) JAV. We also investigate analogous methods when been considered, e.g., Bayesian sampling methods, but not yet those that 64/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info combine profile likelihoods, particularly penalized profile likelihoods, which can remove first order biases and handle separation of data sets. To fill the gap, we consider the combination of penalized likelihood profiles by expressing them as posterior distribution functions (PDF) obtained via a chisquared approximation to the penalized likelihood ratio statistic. PDFs from multiple imputations can then easily be averaged into a combined PDFc, allowing confidence limits for a parameter β at level 1-α to be identified as those β* and β** that satisfy PDFc(β*)=α/2 and PDFc(β**)=1-α/2. We demonstrate that this, "CLP", method outperforms RR in analyzing both simulated data and data from our motivating example. CLP can also be useful as a confirmatory tool, should it show that the simpler RR are adequate for extended analysis. We also compare the performance of CLP to Bayesian sampling methods using Markov chain Monte Carlo. CLP is available in the R package logistf. inconsistent estimates of the parameters of the model of interest, while the `just another variable' approach gives consistent results only for linear models and only if data are missing completely at random. Furthermore, simulation results suggest that even under imputation model mis-specification our proposed approach gives estimates which are substantially less biased than estimates based on passive imputation. The proposed approach is illustrated using data from the National Child Development Survey in which the analysis model contains both non-linear and interaction terms. C25.4 Congenial multiple imputation of partially observed covariates within the full conditional specification framework Jonathan Bartlett1, Shaun Seaman2, Ian White2, James Carpenter1 1 London School of Hygiene & Tropical Medicine, London, UK, 2MRC Biostatistics Unit, Cambridge, UK Multiple imputation (MI) is an increasingly popular tool for handling missing data, but there is a scarcity of tools for checking the adequacy of imputation models. The models used for imputation are statistical models similar to those used in other contexts and so it seems important to consider whether they adequately fit the data to which they are applied. The Kolmogorov-Smirnov test has been identified as a potentially useful diagnostic tool for flagging instances where the distribution of imputed values deviates from that of the observed values of a variable with missing data. Although this test is gaining some recognition in the MI setting, its usefulness as an imputation diagnostic has not been formally evaluated. We assessed its performance in the simple simulation setting of a univariate regression model in which the single covariate was subject to missing data, inducing missingness under MCAR and MAR mechanisms. Although the test was clearly able to flag differences between the observed and imputed distributions, these differences were very weakly associated with the validity of estimation of the regression coefficient. Indeed, with data MAR one expects distributions to differ while MI should correct bias in estimation. We conclude that simple automated flagging of distributional differences is not a useful approach to imputation diagnostics. More focused approaches are needed, such as the method of posterior predictive checking (He & Zaslavsky, 2012), which directly assesses the extent to which inferences of interest are consistent with the imputation model. Missing covariate data is a common issue in epidemiological and clinical research, and is often dealt with using multiple imputation (MI). When the analysis model is non-linear, or contains non-linear (e.g. squared) or interaction terms, this complicates the imputation of covariates. Standard software implementations of MI typically impute covariates from models that are uncongenial with such analysis models. We show how imputation by full conditional specification, a popular approach for performing MI, can be modified so that covariates are imputed from a model which is congenial with the analysis model. We investigate through simulation the performance of this proposal, and compare it to passive imputation of non-linear or interaction terms and the `just another variable' approach. Our proposed approach provides consistent estimates provided the imputation models and analysis models are correctly specified and data are missing at random. In contrast, passive imputation of non-linear or interaction terms generally results in C25.5 Diagnosing the goodness-of-fit of models used for multiple imputation Cattram Nguyen1, Katherine Lee1,2, John Carlin1,2 1 University of Melbourne, Melbourne, Australia, 2Murdoch Children's Research Institute, Melbourne, Australia Afternoon sessions (I6 , C26 - 34) overcome this limitation. I6 Genomics and systems biology I6.1 Nonparametric Bayesian Modelling in Systems Biology Katja Ickstadt Faculty of Statistics, TU Dortmund University, Germany This contribution will begin with a short introduction to nonparametric Bayesian modelling, including generalized Dirichlet process mixture models as well as Poisson/gamma models. We will then introduce two specific applications from systems biology in more detail. The first application centers around the problem of spatial modelling of protein structures on the cellular membrane. Spatial effects such as clustering are supposed to influence signal transmission. Here, a variation of the Dirichlet process mixture model with mixtures of multivariate normals will be employed in order to understand the cluster structure of Ras, a small protein adherent to the plasma membrane. The main goal of the second example is to understand how several components of a biological network are connected. In particular, we will study cell-matrix adhesion sites. Bayesian networks are a main model class for such problems, however, they have the drawback of making parametric assumptions. We will employ a nonparametric Bayesian network approach to In both examples, generalizations of the Dirichlet process prior, like the PitmanYor prior, for nonparametric Bayesian inference will be discussed. Also, biological prior knowledge will be incorporated into the nonparametric Bayesian models. I6.2 Reliable Preselection of Variables in High-dimensional Penalized Regression Problems by Freezing Linn Cecilie Bergersen1, Ismaïl Ahmed2, Arnoldo Frigessi3, Ingrid K. Glad1 and Sylvia Richardson4. 1 Department of Mathematics, University of Oslo, Norway, 2Inserm, CESP Centre for Research in Epidemiology and Population Health, France, 3 Department of Biostatistics, University of Oslo, Norway, 4Department of Epidemiology and Biostatistics, Imperial College London, UK Relating genomic measurements as gene expressions or SNPs to a specific phenotype of interest, often involves having a large number P of covariates compared to the sample size n. While P>>n problems can be solved by penalized regression methods like the lasso, challenges still remain if P is so large that the design matrix cannot be treated by standard statistical software. ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info For example in genome-wide association studies, the number of SNPs can be more than 1 million and it is often necessary to reduce the number of covariates prior to the analysis. This is often called preselection. We introduce the concept of freezing which enables reliable preselection of covariates in lasso-type problems. Our rule works in combination with cross-validation to choose the optimal amount of tuning with respect to prediction performance and finds the solution of the full problem with P covariates using only a subset P'<<P of them. By investigating freezing patterns, we are able to avoid preselection bias, even if variables are preselected based on univariate relevance measures connected to the response. We demonstrate the concept in simulation experiments and observe impressive data reduction rates, without loosing variables that are actually selected in the full problem. We also apply our rule to genomic data, including an ultra high-dimensional regression setting where we are not able to fit the full regression model. I6.3 Using Heritability Analysis to Devise a Prediction Model for Epilepsy Doug Speed University College London Genetics Institute, UK There is continued discussion regarding the so called "missing heritability" problem. By applying a linear mixed model to whole-genome SNP data, a series of papers headed by Yang et. al. have presented strong evidence that many complex traits are highly polygenic, so that while common variants can explain most of the heritability, each on average has such a small contribution to make their detection by standard size GWAS almost impossible. The weight of evidence offered by Yang et. al.'s findings hinge on the reliability of the Linear Mixed Model for heritability estimation. By testing the method under a range of scenarios, we have found in general the technique to be highly robust; the exception is when applied to rare variant traits, but we have developed a fix which leads to dramatically improved performance in this case. By applying the linear mixed model to our epilepsy data, we have determined that, even though the condition is almost certain to be highly polygenic, nonethe-less, useful prediction models should be feasible. In this talk, we discuss our work on heritability estimation. We show how this allows us to narrow down the search for variants and pathways which influence an individual's susceptibility to epilepsy, and facilitates a practical model for predicting whether single-seizure individuals will subsequently develop the condition. Reference: Common SNPs explain a large proportion of the heritability for human height; J. Yang, P. Visscher et. al., Nature Genetics 2010 C26 Competing risk C26.1 Decomposing number of life years lost according to causes of death Per Kragh Andersen Department of Biostatistics, University of Copenhagen, Copenhagen, Denmark The standard competing risks model is studied and we show that the cause j cumulative incidence function integrated from 0 to t has a natural interpretation as the expected number of life years lost due to cause j before time t. This is analogous to the t-restricted mean life time which is the survival function integrated from 0 to t. The large sample properties of a non-parametric estimator are outlined, and the method is exemplified using a standard data set on survival with malignant melanoma. It is discussed how the number of years lost may be related to subject-specific explanatory variables in a regression model based on pseudo-observations. The method is contrasted to causespecific measures of life years lost used in demography. 65/156 C26.2 Parametric modelling of the cumulative incidence function in competing risks models Paul Lambert, Sally Hinchliffe, Michael Crowther University of Leicester, Leicester, UK Competing risks occur when an individual is at risk of more than one type of event. With such data interest often lies in estimation and modelling of the cause-specific cumulative incidence function (CIF), i.e. the cumulative probability of a particular event occurring in the presence of other competing events. Geskus (Biometrics 2011;67:39-49) showed that by simple data expansion together with a time-dependent weighted Kaplan-Meier method it is possible to directly estimate the CIF. In addition, Geskus showed that fitting a Cox model with time dependent weights to the expanded data is equivalent to the competing risks model of Fine and Gray, thus giving estimates of subdistribution hazard ratios. The aim of this work is to demonstrate that similar ideas of data expansion with a weighted likelihood can be used for parametric survival models. This opens up many more opportunities for modelling CIFs using standard parametric survival analysis tools. We illustrate the approach by fitting a flexible parametric survival model with time-dependent weights. The model uses restricted cubic splines to model the log baseline cumulative subdistribution hazard function, providing smooth estimates of the CIF. One important advantage of the approach is that it is easily extended to model time-dependent sub-hazard ratios. In addition, models on other scales, such as a proportional odds model that incorporates splines for the baseline, are easily implemented. Simulation studies show that these models have good statistical properties in terms of bias and coverage. C26.3 Nested case-controls studies in cohorts with competing events Martin Wolkewitz1, Mercedes Palomar2, Ben Cooper3, Martin Schumacher1 1 Institute of Medical Biometry and Medical Informatics, Freiburg, Germany, 2 Universitat Autonoma de Barcelona, Barcelona, Spain, 3Mahidol University, Bangkok, Thailand The nested case-control design is the most widely used method of sampling from epidemiological cohorts. Incidence density sampling is the timedependent matching procedure to create such a nested case-control study. For each case, controls must be disease free at the time of diagnosis of the case to which they are matched. The potential impact of exposures on disease occurrence can then be studied in a reduced data set via conditional logistic regression. This method allows estimation of the incidence rate-ratio (or hazard ratio) which would be received from a Cox regression model applied on the full cohort. However, often the observation of the disease of interest is preceded by other 'competing' events which prevents us from observing the disease of interest. Two approaches deal with competing event data: the event-specific hazard approach which addresses the aetiological point of view and the subdistribution hazard approach which is linked to the cumulative incidence function; the latter is suitable for prediction. We extend previous work by Lubin (Biometrics. 1985;41:49-54) who studied nested case-control studies in a competing event setting by focussing on the event-specific hazard approach. Here, we propose a sampling method for the sub-distribution hazard approach and suggest a time-dependent version of the cumulative incidence sampling by combining two sampling procedures: cumulative incidence and incidence density sampling. The methodology is illustrated by a hospital infection example. 66/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info C26.4 C27 Statistics for epidemiology II Late entry bias in cohort studies with competing endpoints C27.1 Giorgos Bakoyannis, Giota Touloumi, on behalf of CASCADE Collaboration in Modeling changes in cancer risk with time from diagnosis of a family member EuroCoord Marie Reilly1, Myeongjee Lee1, Paola Rebora2, Kamila Czene1 Athens University Medical School, Athens, Greece 1 Karolinska Institutet, Stockholm, Sweden, 2University of Milano-Bicocca, In many cohort studies with competing endpoints, individuals are recruited after Monza, Italy the onset of risks under study. For example, when studying the incidence of AIDS and non-AIDS related death in HIV infected individuals, subjects are It is well accepted that the diagnosis of cancer confers an increased risk on recruited at some time after their corresponding seroconversion dates. This family members, but there are many unanswered questions concerning how phenomenon is known as left truncation in survival analysis. In such settings, this increased risk depends on the age at diagnosis of the index patient, the individuals are under follow-up conditional on having survived at least until their age of the relative(s) at risk, and the time since the index diagnosis. Using the recruitment to the study. Such a conditioning may induce late entry bias. In this Swedish cancer register to identify cancer patients, and the Swedish Multiwork we define the basic structure of late entry bias in competing risks studies Generation register to link relatives, we extracted data on families with any one through Directed Acyclic Graphs (DAGs) and investigate the extent of bias in of five major cancers (colorectal, lung, breast, prostate, melanoma) and the covariates' effect estimates, under the Fine-Gray model, through simulation matched controls from families who were free of cancer on the date of the index diagnosis in the case family. The increased risk of cancer in the case experiments. It can be shown that late entry bias is a form of selection bias induced through families compared to control families was estimated as IRRs from Poisson conditioning on survival status at recruitment. In the simple case of a unique regression models with time since the index diagnosis as smoothed splines, covariate and two competing endpoints, late entry bias in the covariate's effect and from flexible parametric survival models of the time from the index date to estimate is induced if the covariate affects entry time or the hazard of the cancer in relatives. The overall familial risk estimates confirmed the published competing event. Simulation studies showed that there is substantial bias in values for the five cancers. The risk profile for family members was found to be the effect estimates under a Fine-Gray model, as well as low empirical approximately constant for up to 20 years for colorectal, breast, and lung coverage probabilities, if the covariate affects entry time or the hazard of the cancer, but there was evidence of a small decline in risk in the first 5 years for competing event. The magnitude of the bias is analogous to the magnitude of melanoma and a sharp decline for prostate cancer, consistent with a lead-in the effect of the covariate on entry time or on the occurrence of the competing bias from screening of family members. These results can contribute to the genetic counseling and optimal screening of family members of cancer risk. patients. C26.5 C27.2 Inverse probability weighted estimators in survival analysis A comprehensive model for jointly estimating familial risk in all first-degree Ronald Geskus1,2 relatives 1 Academic Medical Center, Amsterdam, The Netherlands, 2Amsterdam Health Myeongjee Lee1, Paola Rebora2, Kamila Czene1, Maria Grazia Valsecchi2, Service, Amsterdam, The Netherlands Marie Reilly1 In the analysis with right censored and/or left truncated time-to-event data, the 1Karolinska Institutet, Stockholm, Sweden, 2University of Milano-Bicocca, hazard plays a predominant role. The reason is that estimation of the hazard is Monza, Italy straightforward: the numerator is the observed number of events and the denominator is the observed number at risk. In a competing risks setting, Familial aggregation is usually evaluated by means of standardised incidence estimation of the subdistribution hazard is more complicated, since individuals ratios of the disease of interest in relatives of affected individuals. This who experience a competing event remain in the risk set until their (usually approach has the advantage of being simple to implement and interpret but it has several limitations: it does not account for familial correlation and does not unobserved) censoring time. provide a formal statistical test to compare the risk in different relatives. An In both settings, the standard nonparametric maximum likelihood estimator alternative method has been proposed (Pfeiffer 2004) where the familial risk is (NPMLE) of cumulative incidence has an algebraically equivalent estimated by the relative risk of first degree relatives of diseased individuals representation as a weighted empirical cumulative distribution function (cases) compared to relatives of a random sample of unaffected controls who (WECDF). Weights are determined by a redistribution of probability mass over may be matched with the cases. The Cox model can be applied and where the future event times for censored cases. Furthermore, left truncation causes study is based on population registers, bootstrapping can be used to account everything that is observed to be reweighted in order to compensate for missed for the matching and the possible relatedness of cases. We have extended this individuals (Geskus, 2011). The equivalence allows for an alternative approach approach using interaction terms that enable the formal comparison of the risks to estimation with some pleasant characteristics. For example, the estimator of for different relationships within an affected family. We present these risks the subdistribution hazard that is used in the Fine and Gray model (1999) for graphically on a pedigree plot. We applied the method to a study of the competing risks follows immediately. aggregation of adult leukaemia in the Swedish population No overall More complicated schemes occur as doubly truncated, doubly censored and aggregation was found for myeloid leukemia, while for lymphatic leukemia the interval censored data. Here, the hazard plays a much less important role. With familial aggregation was high (hazard ratio 5.42). Focusing on chronic doubly truncated data, we show that the expression for the NPMLE as derived lymphatic leukemia, we found evidence that the familial risks for different family by Shen (2010) is of the weighted ECDF form and a one-step estimator is members were significantly different, which can help provide insight into the easily derived. Whether the NPMLE has a weighted ECDF form in case of contribution of genes and environmental factors to the risk of this disease. doubly or interval censored data is unclear. Some ideas in this direction are discussed. C27.3 Multinomial multi-latent-class model. Application to multiple exposures in occupational setting and the risk of several histological subtypes of lung cancer ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info Josué Almansa, Lützen Portengen, Roel Vermeulen IRAS. Utrecht University, Utrecht, The Netherlands Given the individual heterogeneity in large epidemiologic samples, it could be expected that the effect of a certain exposure varies across the entire population, maybe largely caused by unobserved information (e.g. genetic predisposition or lifestyle). Latent class modeling allows classifying individuals according to observed and unobserved heterogeneity. This study assesses the effect of different lifetime-cumulative exposures on the risk of lung cancer in occupational environment. A multinomial logistic regression estimates the probability of each histological cancer-subtype (Squamous cell, Small cell, Adeno-carcinoma and Others). The model includes two types of latent class variables: first, a class-exposure summarizes the most relevant combination patterns of the multivariate exposure measurements; second, each histological cancer-subtype has associated a latent class variable (cancer-subtype-class) so that the effect of class-exposure and covariates on cancer-subtype can vary across the population. It was found that all combinations of the exposure measurements where summarized by 8 (latent) patterns. Moreover, each of the cancer-subtype-class variables defined two subpopulations: with and without risk of its cancersubtype. These cancer-subtype latent classes could also be understood as discrete frailty variables. Among those in the risk-class, there was a significant effect of lifetime-cumulative exposures only for squamous and small cell. Compared to a model without cancer-subtype latent classes, our results showed that there is a part of the population that, even being exposed, they have no risk of lung cancer. Moreover, the OR's of cancer associated to the exposures within the risk-classes were larger than the ones obtained from a (one-class) model for the entire population. 67/156 C27.5 A parametric approach to the reporting delay adjustment method applied to drug use data Albert Sanchez-Niubo1, Alessandra Nardi2, Antònia Domingo-Salvany1, Gianpaolo Scalia-Tomba2 1 Drug Abuse Epidemiology Research Group, IMIM-Hospital del Mar, Barcelona, Spain, 2Dept. of Mathematics. University of Rome Tor Vergata, Rome, Italy In its classical form, the reporting delay adjustment method (Brookmeyer R et al. Am J Epidemiol 1990;132:355-65) allows simultaneous estimation of cohort sizes and a non-parametric lag-time distribution based on reported data, classifiable as to onset period (cohorts), during a sequence of reporting periods. Since counts for more recent cohorts are truncated at the moment of analysis, the estimated lag-time distribution is used to estimate the as yet missing part of each cohort, thus "adjusting" for the delay between onset (cohort) and reporting. This method has been used with drug use data, where the onset refers to the start of drug use and the reporting time to first contact with a treatment centre. In the classical approach, if one wishes to have the lag-time distribution as a proper distribution, one must assume that the longest observed delay corresponds to the right end of the support of the distribution and that this distribution then stays the same for all cohorts. We have opted for a parametric approach, where the delays are modelled as e.g. truncated Weibull distributions, discretized to fit the reporting periods. This approach allows estimation of parameters for each cohort and thus changes in, say, average lag-time can be monitored. Smoothing of the time series of parameters has been considered, as well as the possible role of the choice of parametric distribution. The goodness of fit of the proposed models to the observed data will be evaluated via residual analysis. Examples of application to Spanish data will be presented. C27.4 Modelling the age-dependence of risk in a self-controlled case series analysis C28 Model selection II Katherine Lee1,2, John Carlin1,2 1 2 C28.1 (Scientist award winner) Murdoch Childrens Research Institute, Melbourne, Australia, The University of Melbourne, Melbourne, Australia Robust Gene Selection Based on Minimal Shrinkage Redundancy Since the withdrawal of an earlier vaccine against rotavirus (the leading cause Jan Kalina, Zdenek Valenta of gastroenteritis), several studies have examined evidence for an association Institute of Computer Science AS CR, Prague, Czech Republic between current rotavirus vaccines and risk of intussusception (a rare bowel obstruction) in infants. The self-controlled case series (SCCS) method is a Dimension reduction is a common procedure in the analysis of gene statistical approach to investigate associations between acute outcomes and expression measurements. However, usual gene selection methods have a transient exposures, and has been widely applied to assess potential vaccine tendency to pick gene sets with an undesirable redundancy, which weakens side-effects. The method uses cases only, and compares exposed time at risk the performance of consequent classification methods. The Minimum (e.g. 21 days following a vaccine) with time at risk outside this window within Redundancy Maximum Relevance (MRMR) criterion was proposed to minimize an individual, using conditional Poisson regression. The risk of outcome the gene set redundancy in the process of gene selection. Usual relevance and generally varies with age and it is important to allow for this within the analysis. redundancy criteria are either too sensitive to noise or presence of outlying The standard approach is to split the observation period into age categories, measurements (mutual information, F test statistic) or inefficient (Spearman and allow a separate risk in each category using indicator variables. rank correlation coefficient). Therefore alternative approaches to the MRMR Alternatively, we propose using a fractional polynomial to fit a smooth curve dimension reduction are highly desirable. across the age categories. We compare these two approaches fitted to varying We investigate novel measures of relevance and redundancy, which are based on modern statistical estimation methods. We propose a shrinkage version of numbers of age categories using simulation. We demonstrate that fractional polynomials are more efficient than indicators, the coefficient of multiple correlation and use it as a measure of redundancy of and can lead to more reliable inference, particularly when there are few cases. a gene set. Another proposal is a highly robust correlation coefficient based on In contrast, if there is an abundance of cases fractional polynomials may bias the least weighted squares regression with adaptive weights. This method has the estimated exposure-outcome relationship if the age categorisation is a high breakdown point, which is a crucial statistical measure of sensitivity against noise or influential outliers in the data. Our MRMR criterion combining coarse. these approaches is called Minimum Shrinkage Redundancy Maximum Robust We conclude that fractional polynomials provide a viable alternative for Relevance (MSRMRR). The method is illustrated on gene expression modelling age in an SCCS analysis, but highlight the importance of exploring measurements in a study on patients with cerebrovascular stroke. The new the sensitivity of results to the adjustment method and the number of criterion outperforms standard relevance and redundancy measures, categories used. particularly for gene expression measurements contaminated by noise. 68/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info C28.2 Boosting for variable selection in structured survival models Mar Rodriguez-Girondo1, Thomas Kneib2, Carmen Cadarso-Suárez3, Emad Abu-Assi4 1 Univesity of Vigo, Vigo, Spain, 2Georg-August-Universität Göttingen, Göttingen, Germany, 3University of Santiago de Compostela, Santiago de Compostela, Spain, 4Hospital Clínico Universitario de Santiago, Santiago de Compostela, Spain The RLS method of feature selection were used, among othrs, in designing regression (prognostic) models on the basis of genetic data sets combined with censored survival time. This work was supported by the NCBiR project N R13 0014 04 Feature subset selection linked to linear separabilty Bobrowski Leon1,2 1 Computer Science Department, Białystok University of Technology, Bialystok, Poland, 2Institute of Biocybernetics and Biomedical Engineering, PAS, Warsaw, Poland We present a multiple testing method for hypotheses that are ordered in space or time. This method combines tests of individual hypotheses with global tests for intervals of consecutive hypotheses. Although one usually aims at rejecting individual hypotheses, these rejections cannot always be made because of too small individual effects. Assuming that consecutive hypotheses will provide similar information, it can be beneficial to perform global tests on interval hypotheses in which the individual effects in a particular interval are combined. These interval hypotheses might capture enough information as to get rejected, even if this does not hold for the elements they consist of. To be able to test all individual hypotheses as well as all interval hypotheses while still controlling the familiywise error rate, we apply the sequential rejection principle and make use of logical relationships present in the set of hypotheses. We start at testing the global null hypothesis and when this hypothesis can be rejected we continue with further specifying the exact location/locations of the effect present. The final results enable us to derive statements on how many hypotheses in a certain interval have to be false. The method is best applied to data in which neighboring covariates are expected to behave similarly and where intervals of covariates are of intrinsic interest. For example SNP (single nucleotide polymorphism) data, where intervals of SNPs might indicate genes or, on a higher level, full chromosomes. The method is implemented in R and can be used on various data types. C28.4 Predictive genomic signatures: Biomarker discovery in high-dimensional data Wiebke Werft, Martina Fischer, Axel Benner To improve both prognosis and clinical management of acute myocardial German Cancer Research Center, Heidelberg, Germany infarction patients, an accurate assessment of the different prognostic factors is No treatment works the same for every patient. Few therapies will benefit all required. patients, and some may even cause harm. Hence, biological markers Recent developments of flexible methods for survival analysis such as the ("biomarkers") are required that can guide patient tailored therapy. Using omics structured hazard regression models based on penalized splines allow for a technologies the challenge is to derive a predictive genomic signature from a flexible modeling of the variables affecting survival. Moreover, these models large number of candidates. enable for the inspection of possible interactions between prognostic factors and the assessment of time-dependent associations. Despite their immediate Commonly the identification of potentially predictive biomarkers is addressed appeal in terms of flexibility, these models introduce additional difficulties when by inference of regression models including interaction terms between the a subset of covariates and the corresponding modeling alternatives have to be (continuous) biomarkers and the treatment assignment. To derive a prediction model based on a list of potentially predictive biomarkers we propose to chosen. combine componentwise screening with a final modelling step comprising a We propose a boosting algorithm for model selection in the structured survival forward stepwise selection of interactions. regression framework. Our proposal allows for data-driven determination of the amount of smoothness required for the nonlinear effects and combines model To screen for predictive biomarkers we investigated several extensions to selection with an automatic variable selection property. For computation standard approaches including multivariable fractional polynomials, convenience, we propose to use the piecewise exponential representation for concordance regression, and the application of the permutation of regressor censored survival times which enables to use a Poisson-likelihood boosting residuals test. In the modelling step grouped penalization was applied. approach. The performance of our approach was assessed via an intensive We used simulation studies to assess the utility of the proposed procedures. simulation study. Finally, we apply this method to propose a prognostic model Applications to two prospectively planned, randomized clinical trials will to predict mortality after discharge for patients who suffered an acute illustrate our findings. myocardial infarction. We analyze previously established cardiovascular risk factors, jointly with other less investigated clinical factors as bleeding, an C28.5 increasing incident consequence of the widespread use of aggressive A multiple testing method for ordered data management in these patients in recent years. Rosa Meijer, Jelle Goeman LUMC, Leiden, The Netherlands C28.3 Feature selection procedures are aimed at neglecting of the largest possible number of those features (measurements) which are irrelevant or redundant for a given classification or prediction problem. Feature selection problem is particularly challenging in exploration of genetic data sets. Here we are considering the relaxed linear separability (RLS) method of feature subset selection based on minimization of the convex and piecewiselinear (CPL) criterion functions. This approach refers to the concept of linear separability of the learning sets and is considered in the framework of pattern recognition and data mining methods. The RLS procedure was applied, among others, to the Breast Cancer data set which contains descriptions of 46 cancer and 51 non-cancer patients (van't Veer, L. J., et al., 2002). Each patient was characterized in this set by n = 24481 genes. The RLS method allowed to select the optimal subset of n1 = 12 genes and to find a linear combination (the linear key) of these genes, which allows to correctly distinguish cancer from non-cancer patients in this set - with 100% accuracy. This example demonstrates the ability to use data mining techniques based on the CPL criterion functions also when the number of features is many times greater than the number of objects (Bobrowski, Lukaszuk, INTECH 2011 ). ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info 69/156 coarsening mechanism, where coarsening combines missingness in the longitudinal outcome and censoring in the time-to-event. The absence of such C29.1 a taxonomy exists despite the fact that, as illustrated here, the problem of joint Goodness-of-fit tests for a semiparametric model under a random double modeling of longitudinal and time-to-event data is conceptually similar to that of truncation missing data. An extended shared-parameter joint model is proposed. Under this framework and in contrast to the conventional one, a characterization of Carla Moreira1,3, Jacobo de Uña- Álvarez1, Ingrid Van Keilegom2 MAR is proposed. An intuitively interpretable and hence appealing sub-class is 1 University of Vigo. Department of Statistics and O.R., Lagoas - Marcosende, derived. The developments are illustrated using data collected from a study on 2 36 310 - Vigo, Spain, Institute of Statistics, Biostatistics and Actuarial liver cirrhosis. Sciences, Université catholique de Louvain, Voie du Roman Pays 20,B 1348 Louvain-la-Neuve, Belgium, 3University of Minho, Campus Azurém C29.3 Guimarães, Portugal Randomly truncated data frequently appear in Epidemiology and Survival LIKELIHOOD BASED ESTIMATION FOR AN EFFECT OF A TIME-VARYING Analysis. This happens for instance when the lifetimes or inter-event times at COVARIATE hand correspond to events falling in some observational window. Whe the Ikuko Funatogawa1, Takashi Funatogawa2 observational window is bounded by both ends one gets doubly truncated data. 1Teikyo University Graduate School of Public Health, Tokyo, Japan, 2Chugai AIDS incubation times is a typical example of such data, because in practice Pharmaceutical Co., Ltd., Tokyo, Japan AIDS diagnosis is often restricted to a certain interval of calendar time. Another example of double truncation is found in the analysis of age at diagnosis of a In some clinical studies, the therapeutic agent is administered repeatedly, and disease, e.g. childhood cancer. In these settings with doubly truncated data, doses are adjusted in each patient, based on repeatedly measured continuous the lifetime distribution may be estimated through the Efron-Petrosian NPMLE responses, to maintain the response levels in a target range. Under the or on the basis of some parametric model for the truncation times, which leads response-dependent dose-modification, it was unknown whether the maximum to the Moreira-de Uña-Álvarez semiparametric maximum-likelihood estimator likelihood estimators for dose-response relationship are consistent or not. (SPMLE). The SMPLE outperforms the NPMLE when the paramertic Estimation methods of an effect of a time-varying treatment have been studied information is correct; however, it may be largely biased when the parametric in an area of causal modeling in epidemiology. In this area, mixed effects family is misspecified. In this work we propose goodness-of-fit tests for this models have not been used for the measurement process and non-likelihood semiparametric model. Several testing methods are introduced and compared based estimation methods have been used. In this study, we show that the through Monte-Carlo simulations. The main conclusion is that the proposed maximum likelihood estimators of mixed effects models with dose as a timemethods respect the significance level well, while being able to detect dependent covariate are consistent when the selection of the dose depends on misspecifications in the parametric model as the sample size increases. Real the observed, but not on the unobserved, responses. By simulation studies, we confirm the property of the maximum likelihood estimators in an autoregressive data illustrations are provided. linear mixed effects model (Funatogawa I et al. Statistics in Medicine 2007, 2008, 2012; Funatogawa T et al. Statistics in Medicine 2008). This model is an C29.2 extension of transition models and linear mixed effects models and it can A Framework for Characterizing Missingness at Random in Generalized express profiles approaching asymptotes in each subject. We also confirm the Shared-parameter Joint modeling Framework for Longitudinal and Time-to- property of the maximum likelihood estimators in a linear mixed effects model Event Data under the assumption that all responses are measured at steady state. Edmund Njeru Njagi1, Geert Molenberghs2, Geert Verbeke3, Mike G. Kenward 4, Dimitris Rizopoulos5 C29.4 1 I-BioStat, Universiteit Hasselt, B-3590 Diepenbeek, Belgium, 2I-BioStat, Cost-Sensitive Maximum Likelihood Classification: Finding Optimal Biomarker Universiteit Hasselt and I-BioStat, Katholieke Universiteit Leuven, B-3590 Combinations in Screening and Diagnosis Diepenbeek and B-3000 Leuven, Belgium, 3I-BioStat, Katholieke Universiteit Bruce Tabor, Michael Buckley Leuven and I-BioStat, Universiteit Hasselt, B-3000 Leuven and B-3590 4 Diepenbeek, Belgium, Department of Medical Statistics, London School of CSIRO Mathematics Informatics and Statistics, Sydney, NSW, Australia Hygiene and Tropical Medicine, London WC1E7HT, UK, 5Department of Building a screening or diagnostic test involves estimating an optimal "decision Biostatistics, Erasmus University Medical Center, NL-3000 CA Rotterdam, The boundary" to minimise overall misclassification cost, usually with unequal false Netherlands positives and false negative costs. When a single biomarker provides Models for the analysis of incomplete data are often classified into selection, inadequate discrimination, statistical algorithms such as logistic regression may pattern-mixture, and shared-parameter frameworks, and, in each of these, a be used to estimate a linear combination of variables. Systematically adjusting taxonomy that characterizes the missingness mechanism has been developed, the prior class probabilities (the intercept) will trade-off sensitivity for specificity, leading to the classes of missing-value mechanisms: missing completely at forming an ROC curve. Minimum cost in screening and diagnosis is often near random (MCAR), missing at random (MAR), and missing not at random the extremes of one class (high specificity or sensitivity), implicitly assuming (MNAR). Joint modeling of longitudinal and time-to-event data has largely model validity under such extrapolation, but suboptimal otherwise. focussed on a shared-parameter framework, in which a sub-model for the time- We present an approach that achieves near-optimal multi-marker costto-event process is linked to one for the longitudinal process through a sensitive classification among linear classifiers. The method uses binomial common latent structure, conditional on which independence is assumed. regression where the link function is chosen so that the implied loss functions Though inference under this framework has customarily made an assumption (the "deviance" of each class with respect to the linear predictor) are sigmoid which mimics MAR, the so-called assumption of non-informativeness of approximations of the optimal Bayes loss functions with appropriate costcensoring and the visiting process, nevertheless, the current framework defies weightings. These sigmoid loss functions belong to single parameter family that an elegant characterization of MAR. An unambiguous taxonomy has not been includes the logistic and exponential losses, a property that is exploited to developed. Such a taxonomy, analogous to the missing data context, would solve this non-convex problem. A serendipitous result is a form of logistic classify these models based on the assumptions that they make about the classifier that is robust to outliers, a general feature of this approach. C29 Statistical design and methodology I 70/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info The method is illustrated using simulated multivariate normal distributions with unequal variances, where the optimal linear classifier is accessible via analytic methods, and also with a real dataset. Near-optimal cost-sensitive classification can have a profound effect on both variable selection and weighting in the predicted classification boundary, notably when the assumptions underlying traditional methods are violated. C29.5 Designing a preliminary adaptive study to develop biomarker combinations for trial Toby Prevost1, Jack Bowden2 1 King's College London, London, UK, 2MRC Biostatistics Unit, Cambridge, UK progress but also for trials that have yet to start. This suggests that what would be useful would be to add a higher level of the hierarchy: over all trials. We present one possible approach to doing this using an orthogonal parameterization of the gamma with parameters on the real line. The two parameters are modeled separately. We illustrate this approach using data from 18 trials. We make suggestions as to how this method could be applied in practice and conclude that the key to successful implementation rests with careful analysis of data from a reasonable number of previous clinical trials. C30.2 Evaluation and validation of social and psychological markers: identification and assumptions for instrumental variables estimation Hanhua Liu, Richard Emsley, Graham Dunn Effective biomarkers carry the potential to stratify treatments for patients, Health Sciences Research Group, The University of Manchester, Manchester, thereby providing personalised medicine. Prior to testing in late phase trials, UK biomarkers are typically identified through preliminary studies to inform their predictive potential and required trial parameters. However, biomarker studies Complex intervention trials involve evaluating social and psychological markers as potential prognostic factors, moderators, mediators or candidate surrogate can be prohibitively small for purpose, partly due to cost. outcomes. We focus on using such markers to assess treatment-effect Here we considered the design of a preliminary study of potentially predictive mediation in the presence of measurement errors, hidden confounding biomarkers in patients treated for Psoriasis. The objective was to provide a (selection effects) between post-randomisation markers and outcomes and design allowing multiple biomarkers and their combination to be assessed to missing data. inform any subsequent trial. We compared a non-adaptive prospective design with a one having two stages of patient recruitment, where poorly-performing Instrumental variable methods provide unbiased estimates at the expense of precision, but model identification using more-informative and more-realistic biomarkers can be discontinued after the first stage. models requires potentially invalid assumptions. In particular, aside from Power was assessed through simulation in R-software using Fisher's method, imposing parametric structure, they require: moderation of treatment effects on involving the product of stage p-values. Effect size was defined in terms of the markers by covariates but no moderation of the direct effect of treatment on correlation between treatment response over time and a biomarker. Under a outcome, equivalent to using covariate by treatment interactions as non-adaptive design, an R-squared of 20% could be detected with 90% power, instrumental variables; no moderation of effects of marker on outcome by 5% significance level, with all 17 expensive biomarkers measured in 49 covariates; and no marker by treatment interactions on the direct effect of patients. The adaptive design offered an interesting alternative, employing treatment on outcome. Further, considering the unmeasured confounder as a p>0.3 to discontinue with biomarkers quarter-way through recruitment, post-randomisation rather than baseline variable we consider the effect on requiring 24+72=96 patients. This offered more patients to develop a estimation procedures if the confounder is directly influenced by treatment. combination from an enriched biomarker set, and improved current practice of overly small studies when developing combinations in this clinical area. We perform Monte Carlo simulation studies under a variety of scenarios The proportion of biomarkers expected to be discontinued, conditional on involving selection effects, measurement error and imperfect prediction of underlying effect size, was presented as part of a presentation to the study markers. We weaken these identifying assumptions in turn, and allow team who accepted and implemented the design. treatment to predict the post-randomisation confounder in two ways: a mean change between the treatment groups, and by independently introducing heteroskedasticity. Using these results we provide recommendations C30 Statistical design and methodology II concerning informative designs and on corresponding sample size requirements for marker evaluation in complex intervention trials. We illustrate C30.1 how the methods can be implemented using a randomised trial of cognitive Predicting Patient Recruitment in Multi-Centre Clinical Trials behaviour therapy in psychosis. 2 1 3 Andisheh Bakshi , Stephen Senn , Alan Phillips 1 CRP-Sante, Strassen, Luxembourg, 2University of Glasgow, Glasgow, UK, 3 C30.3 ICON plc, Leopardstown, Ireland Sample size calculation for cluster randomized stepped wedge designs In recent work Anisimov has made impressive progress in modelling patient recruitment in multi-centre clinical trials. He assumes that the distribution of the Esther de Hoop, Willem Woertman, Steven Teerenstra number of patients in a given centre in a completed trial follow a Poisson Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands distribution. In a second stage the unknown parameter is assumed to come In a stepped wedge design, all clusters start in the control condition after which from a gamma-distribution. As is well known the overall gamma-Poisson they switch to the intervention at consecutive time points. Eventually, all mixture is a negative binomial. clusters will have switched to the intervention. This design is especially useful For forecasting time to completion, however, it is not the frequency domain when the intervention is thought to do more good than harm. that is important but the time domain and Anisimov has also illustrated clearly The stepped wedge design is increasingly being used in cluster randomized the links between the two and the way in which a negative binomial in one trials. However, there is not much information available about the design and corresponds to a type VI Pearson distribution in the other. Anisimov has also analysis strategies for these kinds of trials. Approaches to sample size and shown how one may use this to forecast time to completion in a trial in power calculations have been provided, but a simple sample size formula is progress. lacking. Therefore, we will present a sample size approach using a design However, it is not just necessary to forecast time to completion for trials in effect (sample size correction factor). ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info We derived a design effect that corrects for clustering as well as the stepped wedge design. Furthermore, we compared the required sample size for the stepped wedge design with a parallel group design. For the design effect of the stepped wedge design, choices of the number of baseline measurements, the number of measurements between switches and the number of steps have to be made. Furthermore, estimates of the cluster size and intracluster correlation are needed. Increasing the number of measurements and steps decreases the required sample size. However, increasing the cluster size increases the total required sample size. In comparison to a parallel group design, the stepped wedge design is always more efficient in terms of sample size. C30.4 Lifestyle, socioeconomic factors and consumption of dairy foods analysed with structural equation modelling Are Hugo Pripp Unit of Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway 71/156 data set. In the MCFA we used the Data Augmentation approach since the multivariate vector of responses consists of binary responses. Model assessment was carried out on the learning data set to avoid double usage of the data which might lead to conservative goodness of fit inferences. The Bayesian approach is more flexible compared to the frequentist approach. We also proposed the use of Multivariate Analysis of Variance statistics as discrepancy measures to access the need for multilevel modeling. Conclusion: Neglecting the multilevel structure of the data in statistical analysis can lead to invalid inferences. In factor analysis the interpretation and the number of the factors is also level dependent. C31 Longitudinal data C31.1 Fast linear mixed model computations for GWAS with longitudinal data Karolina Sikorska1, Fernando Rivadeneira1, Patrick Groenen2, Paul Eilers1, Emmanuel Lesaffre1,3 1 Erasmus Medical Center, Rotterdam, The Netherlands, 2Erasmus University, Dairy foods are important in the Nordic diet and therefore relevant from a Rotterdam, The Netherlands, 3L-Biostat, Catholic University of Leuven, health perspective. Observed relationships between food preference and Leuven, Belgium health could be due to nutritional properties and/or strongly confounded by other factors. Large cross-sectional studies as The Oslo Health Study Recent genome-wide association studies are directed to identify single (HUBRO) provide important epidemiological data to study the relationship nucleotide polymorphisms (SNPs) associated with longitudinally measured between lifestyle, socio-demographic factors, food preference and health. traits. In our motivating data set, the bone mineral density (BMD) of more than However, presence of missing data, invalidated items or variables that does not 5000 elderly individuals was measured at 4 occasions over a period of 12 specifically describe the factor of interest might limit the use of population- years. We are interested in SNPs that influence the change of BMD over time. based cross-sectional studies for this purpose. Structural equation modelling This could be done by fitting a linear mixed model with covariates age, gender, as statistical technique may both improve measurement uncertainty, etc but also including each of the SNPs at a time. However, fitting 2.5 million of interpretation of items and model complex relationships. It combines elements linear mixed models (1 model per SNP) on a single desktop would take more from linear regression, path analysis and factor analysis and is extensively than a month. used in sociological and psychological research. Especially, it has gained Dealing with such prohibitively large computational time, it is desirable to interest as a method to test and estimate causal relations using statistical data develop a fast technique. We explored a variety of fast computational and causal assumptions. Structural equation modelling was explored on procedures. The best approximating procedure is based on a conditional twoselected variables describing lifestyle, socio-demographic factors and health in step (CTS) approach. This approach approximates the P-value for the SNPrelation to preference of diary foods using data from The Oslo Health Study. time interaction term from the linear mixed model analysis. Our method is Physical activity and an overall healthy lifestyle were usually related to based on the concept of a conditional linear mixed model proposed by Verbeke increased consumption of milk products, but the association with economic et al. (2001). A simulation study shows that this method has the highest income was not so clear. The gained statistical information using structural accuracy of all considered approximations. Applying the CTS approach equation modelling compared to classical generalized linear models are reduced the computational time needed to analyze the BMD data to 5 hours. assessed on data from this large cross-sectional study. We are now exploring the robustness of the CTS against different simulation parameters such as sample size, number of measurements, variancecovariance parameters etc. We are also exploring the performance of the CTS C30.5 in case of more complicated residual errors structure (autocorrelation, Bayesian multilevel factor analytic model for assessing the relationship heteroscedascity). between nurse-reported adverse events and patient safety Luwis Diya1, Baoyue Li3, Koen Van den Heede2, Walter Sermeus2, Emmanuel C31.2 Lesaffre2,3 1 Karolinska Institutet, Stockholm, Sweden, 2Katholieke Universiteit Leuven, A Bayesian Model for Multivariate Human Growth Data Sten Willemsen1, Regine Steegers-Theunissen2, Paul Eilers1, Emmanuel Leuven, Belgium, 3Erasmus MC, Rotterdam, The Netherlands Lesaffre1,3 Aims: Adverse Events (AEs) are considered as proxies of patient safety and 1 are often analyzed separately. However the totality or multivariate vector of Department 2of Biostatistics, Erasmus Medical Centre, Rotterdam, The AEs is a better proxy. Data on AEs is usually multilevel in structure. The aim of Netherlands, Division of Obstetrics and Prenatal Medicine, Department of Gynaecology, Erasmus Medical Centre, Rotterdam, The this study is to explore the relationship between nurse-reported AEs using Obstetrics and Netherlands, 3L-Biostat, Catholic University of Leuven, Leuven, Belgium multilevel factor analysis. Methods: Data from the Belgian chapter of the Europe Nurse Forecasting nurse survey was used to establish the relationship between 6 AEs and patient safety. As there was no a prior factor structure suggested, the data set was split into a learning and a validation data set. We used the learning data set to explore a plausible factor structure using a frequentist Multilevel Exploratory Factor Analysis (MEFA) and we validated this factor structure by using a Bayesian Multilevel Confirmatory Factor Analysis (MCFA) on the validation Having good models of human growth during gestation and childhood is important for distinguishing between normal and abnormal growth. In the SuperImposition by Translation And Rotation (SITAR) approach, individual growth curves are shifted horizontally and vertically and are stretched so they overlap and form an ‘average' growth curve. This ‘average' profile is estimated by means of a natural cubic spline. 72/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info In various growth studies different attributes are measured simultaneously and repeatedly (for example both height and weight). Usually, they are analyzed one outcome at a time. In doing so, the relationship between different types of measurements is ignored. However, it might be useful to model the different growth variables jointly. We have extended the SITAR approach to the multivariate case allowing us to consider the relation between the various measurement types. The model is estimated using an MCMC algorithm. We will apply the method to two sets of growth data. The first is the Jimma Data set which contains data on the height, weight and arm circumference of 495 children in the Jimma region of Ethiopia. The second data set is from the PREDICT study containing crown rump length, embryonic volume and curvature of the embryo measured in early pregnancy. Using our methodology we relate growth to several outcomes (later in life) and determinants. We also use our model to develop multivariate reference curves. As an extension we show how our Bayesian methodology allows us to easily vary some of the underlying assumptions of the model thereby performing a sensitivity analysis. C31.3 Cognitive lifestyle and cognitive decline: the characteristics of two longitudinal models Riccardo Marioni1, Cécile Proust-Lima2, Helene Amieva3, Carol Brayne1, Fiona Matthews4, Jean-Francois Dartigues3, Hélène Jacqmin-Gadda2 1 University of Cambridge, Cambridge, UK, 2INSERM U897, Univ. Bordeaux ISPED, Bordeaux, France, 3INSERM U897, Univ. Bordeaux ISPED, Department of Clinical Neurosciences CHU Pellegrin, Bordeaux, France, 4MRC Biostatistics Unit, Cambridge, UK It is rare for longitudinal analyses of cognitive decline in the elderly to account for death, measurement error of the cognitive phenotype, unequal sensitivity to cognitive change, cognitive recovery, and differing covariates effects at different stages of decline. This study applies two models (a multi-state model and a joint latent class mixed model) that account for these issues to a large, population-based cohort, Paquid, to model more realistically the relationship between cognitive lifestyle and cognitive decline. Cognition was assessed over a 20 year period using the Mini Mental State Examination. Three cognitive lifestyle variables were assessed: education, mid-life occupation, and late-life social engagement. Both approaches found increased education to be associated with a more favourable cognitive trajectory over time. Late-life social engagement, a potentially modifiable factor, was strongly associated with mortality irrespective of cognitive trajectory. Interpretation of parameters from the multi-state model is easy and this approach explicitly models cognitive recovery. However, it requires a priori definition of clinically meaningful cognitive states. By contrast, the mixed model approach enables study of minor changes by using the quantitative cognitive measure and handles heterogeneous population. C31.4 A statistical model to explore carcinogenic processes by transcriptomics in prospective studies Sandra Plancade1, Gregory Nuel2, Eiliv Lund1 1 University of Tromsø, Tromsø, Norway, 2University Paris Descartes, Paris, France Most epidemiological prospective studies collect lifestyle factors and/or genomic data (SNPs), and aim at the estimation of relative risks and prediction sets. In such contexts, survival analysis models - in particular the Cox model which parametrize the failure time given the covariates have proven to be efficient, and have been extended to include time-dependent covariates. Nevertheless, their implementation on gene expression covariates whose distribution might be affected by the carcinogenic process, does not enable a direct biological interpretation, which makes the incorporation of complex models of carcinogenesis difficult. Alternatively, we consider a direct modelling of the gene expression as a function of time to diagnosis conditionally to exposures, which allows more flexibility to build complex statistical models incorporating biological assumptions. We propose a latent variable model, based on the multistage model, which might simultaneously estimate the last-stage length distribution and detect the genes whose expression changes over time. The parameters are estimated by a Stochastic EM algorithm, which shows good results on simulated data. This model constitutes a structure on which correlations between exposures, genomic and transcriptomic data could be integrated with the carcinogenic process. C31.5 Analysis of Change Over Time When Measurements are Obtained Only After an Unknown Delay Andrew Copas, David Dolling, David Dunn MRC Clinical Trials Unit, London, UK In longitudinal studies repeated measurements of a biomarker, Y, may be obtained from participants to assess changes in health, in relation to a participant characteristic X. In HIV disease interest may be in changes after infection. However patients are only observed after an unknown ‘delay' from infection to diagnosis, possibly related to X, but we initially assume conditionally independent of Y values. Right censoring may also arise from starting treatment if interest is in health without treatment. Some authors have proposed the use of external data in combination with biomarker values at diagnosis to deduce the date of infection, but this requires very strong assumptions. When a linear mixed model for Y can be assumed in terms of X and time from infection, T, other authors have naïvely ignored the delays and fitted models based on X and time from diagnosis, T*. Where delay is related to X then such naïve models can be correctly specified for Y and the fixed effect parameters relating to T* and XT* coincide with those for T and XT. However additional fixed and random effects relating to X are induced by the delay. Correctly incorporating these effects is important to provide robustness against right censoring which is often ‘missing-at-random'. Simulations based on real HIV datasets and changes in CD4 count illustrate this point. We conclude that under assumptions the naïve approach ignoring delay can be used for inference concerning change from infection but a flexible fixed and random effects structure should be specified. C32 Survival analysis III C32.1 High-dimensional survival studies - comparison of approaches to assess timevarying effects Anika Buchholz1, Willi Sauerbrei1, Harald Binder2 1 Institute of Medical Biometry and Medical Informatics, University Medical Center Freiburg, Freiburg, Germany, 2Institute of Medical Biostatistics, Epidemiology and Informatics, University Medical Center Johannes Gutenberg University Mainz, Mainz, Germany The development of cohorts including genomic and transcriptomic (mRNA or gene expression) data, as well as lifestyle information, opens new perspectives for the study of carcinogenic dynamics. In particular these designs enable to explore the time-dependent distributions of the gene expression conditionally to exposures . Further on, they give the opportunity to connect epidemiological studies with biological models of carcinogenesis, including the multistage Survival studies with microarray data often focus on identifying a set of genes with significant influence on a time-to-event outcome for building a gene model. ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info expression signature (i.e. predictor). Most of these predictors are usually derived using the Cox proportional hazards (PH) model assuming that effects are constant over time. However, there might be time-varying effects, i.e. violation of the PH assumption, for the predictor and some of the genes. Ignoring this may lead to false conclusions about their influence. Hence, it is important to investigate for time-varying effects. Recently we have compared several strategies for identifying, selecting and modelling time-varying effects in low-dimensional settings [1]. Some of them can also be applied to high-dimensional data and will be illustrated and compared using publicly available gene expression data with time-to-event outcome from cancer patients, for which predictors have been derived [2,3]. In addition, we will investigate whether the time-varying effect of a predictor is mainly caused by some of its components. References: [1] A. Buchholz, W. Sauerbrei. Comparison of procedures to assess non-linear and time-varying effects in multivariable models for survival data. Biometrical Journal, 53:308-331, 2011. [2] C. Desmedt, F. Piette, S. Loi, et al. Strong time dependence of the 76-gene prognostic signature for node-negative breast cancer patients in the transbig multicenter independent validation series. Clinical Cancer Research, 13:32073214, 2007. [3] H. Binder and M. Schumacher. Incorporating pathway information into boosting estimation of high-dimensional risk prediction models. BMC Bioinformatics, 10:18, 2009. C32.2 Frailty modeling of age-incidence curves of osteosarcoma and Ewing sarcoma among individuals younger than 40 years Morten Valberg1, Tom Grotmol2, Steinar Tretli2, Marit B. Veierød2, Susan S. Devesa3, Odd O. Aalen1 1 Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway, 2Cancer Registry of Norway, Institute of Population-Based Cancer Research, Oslo, Norway, 3Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA The Armitage-Doll model with random frailty can fail to describe incidence rates of rare cancers influenced by an accelerated biological mechanism at some, possibly short, period of life. We propose a new model to account for this influence. Osteosarcoma and Ewing sarcoma are primary bone cancers with characteristic age-incidence patterns that peak in adolescence. We analyze SEER incidence data for whites younger than 40 years diagnosed during the period 1975-2005, with an Armitage-Doll model with compound Poisson frailty. A new model treating the adolescent growth spurt as the accelerated mechanism affecting cancer development is a significant improvement over that model. Our results support existing evidence of an underlying susceptibility for the two cancers among a very small proportion of the population. In addition, the modeling results suggest that susceptible individuals with a rapid growth spurt acquire the cancers sooner than they otherwise would have, if their growth had been slower. The new model is suitable for modeling incidence rates of rare diseases influenced by an accelerated biological mechanism. 73/156 Bordeaux, F-33000, France, 4INSERM CIC-EC7, Bordeaux, F-33000, France For many diseases, individuals may experience several and related types of relapses or recurrent events during the life course of the disease. For instance, a breast cancer patient could have locoregional and metastatic relapses. In addition, follow-up may be interrupted for several reasons, including the end of a study, or patients' lost-to-follow-up, which are non-informative censoring events. Death could also stop follow-up, hence, it is considered as a dependent terminal event. Frailty models (Vaupel et al. 1979), which are extensions of proportional hazards survival models for recurrent events, aim to account for potential heterogeneity caused by unmeasured prognostic factors and inter-recurrence dependency through a random effect. Rondeau et al. (2007) and Liu et al. (2004) showed that death process has to be included in a joint modelling framework with the recurrent event process to avoid biases on regression parameters. We propose a multivariate frailty model with possibly time-dependent regression coefficients that jointly analyzes two types of recurrent events with a dependent terminal event. Two estimation methods are proposed: a semiparametrical approach using penalized likelihood estimation where baseline hazard functions are approximated by M-splines, and another one with parametrical baseline hazard functions. We derived martingale residuals to check the goodness-of-fit of the proposed models. We illustrate our proposals with a real data set on breast cancer. The main objective was to measure potential dependency between the two types of recurrent events (locoregional and metastatic) and the terminal event (death) after a breast cancer and to estimate the influence of prognostic factors. C32.4 Sparse partial least-squares regression for high-throughput survival data analysis Donghwan Lee1, Youngjo Lee1, Woojoo Lee2, Yudi Pawitan2 1 Seoul National University, Seoul, Republic of Korea, 2Karolinska Institutet, Stockholm, Sweden The partial least-squares (PLS) method has been adapted to the Cox's proportional hazards model for analyzing high-dimensional survival data. But since latent components constructed in PLS employ all predictors regardless of their relevance, it is difficult to interpret the result. In this paper, we propose a new formulation of the sparse PLS (SPLS) procedure for survival data to allow both sparse variable selection and dimension reduction. We develop a computing algorithm for SPLS by modifying an iteratively reweighted PLS algorithm, and illustrate the method with Swedish breast cancer data. Through the numerical studies we find that our SPLS method generally performs better than the standard PLS and sparse Cox regression methods in variable selection and prediction. C32.5 A Regression Model for the Extra Length of Stay Associated with a Nosocomial Infection Arthur Allignol1, Martin Schumacher2, Jan Beyersmann1,2 1 Freiburg Center for Data Analysis and Modeling, University of Freiburg, C32.3 Freiburg, Germany, 2Institute of Medical Biometry and Medical Informatics, Multivariate frailty models for two types of recurrent events with a dependent University Medical Center Freiburg, Freiburg, Germany terminal event: Application to breast cancer data The occurrence of a nosocomial infection (NI) constitutes a major complication Yassin Mazroui1,2, Audrey Mauguen1,2, Simone Mathoulin-Pelissier1,3,4, Gaëtan that can be severe in terms of mortality/morbidity as well as in terms of Macgrogan3, Véronique Brouste3, Virginie Rondeau1,2 prolonged length of stay (LoS) in the hospital, which is one of the main driver 1 Université Bordeaux Segalen, Bordeaux, F-33000, France, 2INSERM, ISPED, for extra costs due to NI. Information on extra LoS is used in cost-benefit Centre INSERM U-897-Epidémiologie-Biostatistique, Bordeaux, F-33000, studies which weigh the costs of infection control measures like isolation rooms France, 3Unité de recherche et d'épidémiologie cliniques - Institut Bergonié, against the costs raised by NI. Estimation of extra LoS is complicated by the 74/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info fact that the occurrence of NI is time-dependent. Cox proportional hazards models including NI as a time-dependent covariate could be used but do not permit to quantify the number of extra days following an infection. Using the multistate model framework, Schulgen and Schumacher (1996) devised a way to quantify extra LoS comparing the mean LoS given current NI status and averaging this quantity over time. This quantity has foundations in landmarking (Anderson et al., 1983). It has also been extended to the competing risks setting in order to distinguish between discharge alive and death (Allignol et al., 2011). However, a way of studying the impact of covariates on this extra LoS is still lacking. We propose to use the pseudo value regression technique (Andersen et al., 2003). The idea is to use a generalized estimating equation model on the pseudo-values of the extra LoS.Motivated by a recent study on hospitalacquired infection, we investigate the use of pseudo values for identifying additional risk-factors that influence the extra LoS. C33 Statistical methodology II (Ackermann and Strimmer (2009)). The advantages of these so-called sum statistics lie in the applicability in small sample settings and the straightforward interpretation. Here, inference is usually based on resampling methods to account for possible dependencies between the marginal test statistics. Motivated by recent work on global tests for multiple endpoints underlying a multivariate discrete distribution (Agresti and Klingenberg, 2005; Klingenberg et al., 2009), the aim of this talk is to provide two sample sum statistics for testing against marginal inhomogeneity in complex ordinal data. Particular emphasis is put on the discussion of valid inference methods. By means of simulated data we investigate the proposed sum statistics and illustrate some limitations of the popular permutation approach. In case of tree-structured data we show how these sum statistics might be used to reveal significant subsets. We apply the proposed methodology to International Classification of Functioning, Disability and Health (ICF) data where a lot of ordinal variables are usually collected (World Health Organization, 2001). C33.1 (Scientist award winner) Modelling Overdispersion in Wadley's Problem with a Beta-Poisson Distribution Kerry Leask1, Linda Haines2 1 CAPRISA, Durban, KwaZulu-Natal, South Africa, 2University of Cape Town, Cape Town, Western Cape, South Africa C33.3 On-line surveillance of air pollution Eva Andersson Occupational and environmental medicine, Sahlgrenska University hospital and Sahlgrenska Academy, Goteborg, Sweden Wadley's problem frequently emerges in dose-response studies and occurs when the number of organisms surviving exposure to a particular dose of a drug is observed but the number initially treated is unobserved and is estimated from control samples. Data which arise from this problem setting are frequently overdispersed. Historically, Wadley (1949) modelled the number of organisms initially treated using a Poisson distribution. The resulting distribution for the number of survivors is a Poisson model with parameter proportional to the probability of survival. This model cannot accommodate overdispersion. As a means of accommodating overdispersion, Anscombe (1949) used a negative binomial model to model the number of organisms initially treated with the drug. The result is a negative binomial model for the number of surviving organisms. The present study considers an antimalarial drug study conducted by the Medical Research Council in Durban as part of its Malaria National Program. Blood samples were collected from suspected malaria sufferers who reported to clinics in KwaZulu-Natal during April 1989 and March 1990. The samples were treated with varying concentrations of an antimalarial drug and the number of surviving malaria parasites was recorded. The beta-Poisson model is considered for modelling this data set because the traditional Poisson and negative binomial models provided very poor fits. The model is derived from the Poisson by modelling the probability of survival using a beta distribution. Some properties of the model are explored and its fit is compared with those of the Poisson and negative binomial models. Air pollution can cause respiratory problems in both children and adults. Nitrogen oxides (NOx) are formed in combustion and road traffic is the largest source of emissions in the larger urban areas. Sensitive groups include people with previously heart disease, asthma or COPD. Airborne particulate matter (PM) is another much discussed air pollution. There are many sources: transports, small-scale wood burning, use of studded tires and long-distance transport of air pollutants from other countries. Association with cardio-vascular diseases and mortality has been shown. It would be beneficiary with a system for early detection of increased levels or air pollution. By continually monitoring e.g. the daily levels, we may detect changes early. The methodology of statistical surveillance is appropriate, in which an alarm system (alarm statistic and alarm limit) is constructed. The alarm system should, ideally, produce few false alarms and quick motivated alarms. Examples of methods are Shewhart, EWMA and CUSUM. Many surveillance systems are based on the assumption of a process which is independent over time, but many data display autocorrelation. One way of handling serially dependent data is to monitor the residual of the AR-process. Air pollution data, measured hourly and daily are used to develop monitoring systems for NOx and PM. Preliminary results show that the NOxmeasurements during the first hours of a day can be used to predict days with high levels of NOx. For PM, preliminary results show that the residual approach has high detection ability for signaling days with increased levels of particles. C33.2 Global testing for complex ordinal data Ulrich Mansmann, Monika Jelizarow IBE, LMU, Munich, Germany Over the past decade, a plethora of methods have been developed to detect global effects in groups of complex (i.e. multivariate and possibly highdimensional)metric variables such as gene expression or metabolomic data. Less attention, however, has been constrained to the case when the variables of interest are categorical. For the former, the construction of a global test statistic as the sum of univariate test statistics has amply been discussed C33.4 An Improved Algorithm for Outbreak Detection in Multiple Surveillance Systems Angela Noufaily, Doyo Gragn, Paddy Farrington, Paul Garthwaite The Open University, Milton Keynes, UK There has been a great interest in the use of statistical surveillance systems over the last decade, prompted by concerns over bio-terrorism, the emergence of new pathogens such as SARS and swine flu, and the persistent public health problems of infectious disease outbreaks, for example the recent e-coli epidemic in Germany. It is important to detect these outbreaks early in order to take suitable control measures. In England & Wales, an automated laboratory-based outbreak detection system has been in operation since the early 1990s, based on a quasi-Poisson ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info 75/156 regression model (Farrington et al 1996). We propose an improved version of this algorithm for outbreak detection of infectious diseases in large multiple surveillance systems. For a better treatment of trend and seasonality, we extend the existing quasi-Poisson regression model into a 10-level factor. For a more appropriate computation of the prediction intervals, we propose using negative binomial quantiles rather than the normal approximation involving scaled Anscombe residuals. Weaker down-weighting (than the existing) of baseline data using the scaled Anscombe residuals is suggested to reduce the influence of past outbreaks. A new adaptive scheme for re-weighting based on past exceedances is also proposed. Extensive simulations show that the mentioned modelling choices reduce the high proportion of false alarms without impairing the detection of genuine outbreaks. Applications to data sets obtained from the United Kingdom's Health Protection Agency are given. However, it can be shown in simulations that the significance levels often deviate considerably from the nominal level. The assumptions for using the rank correlation test are not strictly satisfied. The pairs of observations fail to be independent, but the main cause of the poor significance level is a correlation between standardized effect sizes and sampling variances under the null hypothesis. We propose alternative rank correlation tests to improve error rates. An unstandardized test directly correlates estimated effect sizes and sampling variances. This test reduces the Type II error rate, unfortunately at the expense of the Type I error rate. Simulations show that the standardized and unstandardized test statistics contain about the same amount of information. In tests for publication bias, it is essential to control the Type II error rate. If the significance level cannot be fixed, the unstandardized test is preferable. Another test is based on the simulated distribution of the estimated measure of association, conditional on sampling variances. Its significance level equals the nominal level and the Type II error rate is reduced compared to the Begg and Mazumdar test. Although more computer intensive, this test attains the best C33.5 significance levels. The use of symmetric and asymmetric distance measures for high-dimensional Begg CB, Mazumdar M. (1994). Operating characteristics of a rank correlation tests of inferiority, equivalence and non-inferiority test for publication bias. Biometrics 50, 1088-1102. 1 1 2 Siegfried Kropf , Kai Antweiler , Ekkehard Glimm 1 Otto von Guericke University Magdeburg, Magdeburg, Germany, 2Novartis C34.2 Pharma AG, Basel, Switzerland Dealing with missing binary outcome data in meta-analysis: application to Tests based on multivariate measures of distance or similarity between sample randomized clinical trials in nutrition elements have proven as powerful instrument for small samples from highAnissa Elfakir, Sebastien Marque dimensional data in many medical and biological applications. In the usual situation of tests for differences, permutation tests can easily be applied as Danone Research, Palaiseau, France nonparametric versions. Under the assumption of multivariate normal Meta-analysis of randomised clinical trials is considered as the gold standard to distributions, parametric rotation tests enable the application even in very small demonstrate the efficacy of a health product. The existing literature describes samples, where the number of possible permutations would not be sufficient for missing data as a critical issue in meta-analysis. Its impact on result validity effective tests. Both permutation and rotation tests are exact tests and perform has been extensively studied and general recommendations have been well with surprisingly small samples. The application will be demonstrated by published. The analysis strategy should be pre-specified; explicit assumptions an example. on missingness mechanism should be made in the specific context of the study New attempts are directed at high-dimensional equivalence tests. In these and research area; the primary analysis should be based on a method, valid situations, strict control of equivalence in each component is a very demanding under the most plausible assumption; sensitivity analyses should be planned condition for equivalence. With realistic sample sizes it is almost impossible to and the potential influence on the results should be discussed. declare equivalence with such a criterion. Therefore we suggest a multivariate Clinical studies in nutrition may require daily reporting of outcomes and on-site approach based on pairwise distance measures between sample elements as visits over several months. Missing data can arise due to premature an alternative strategy. However, due to the change in hypotheses, type I error withdrawals, intermittent missing daily reporting or visit, or other non-specified control is more difficult than for tests of differences because the null hypothesis reasons. Current reporting of meta-analysis in nutrition often inadequately is no longer a single point in the parameter space. We present proposals for handle the missing data issue. For binary outcomes, "complete/available case" appropriate criteria and how to test them based on resampling techniques and analyses are commonly used, and alternatives are based on simple show simulation results. Possible applications are comparisons of multi- imputations. The latter method is inefficient and likely misleading in the species microbial communities. nutritional setting and the formers underestimate the uncertainty of the Finally, we consider asymmetric distance measures which are necessary to estimates. As well, robust methods are rarely used. construct one-sided tests as needed for non-inferiority investigations. Such In the context of a meta-analysis of clinical trials investigating the effect of a problems arise in safety analyses with a large number of variables. nutritional product on a binary outcome, the most plausible missingness mechanism assumption was discussed. Simulations were performed to examine the relative merits of naïve and robust methods, and define a valid C34 Meta-analyses/Combined data sources strategy. This strategy was then applied and its results discussed on a concrete C34.1 example. Improving the error rates of the Begg and Mazumdar test for publication bias in meta-analysis C34.3 Miriam Gjerdevik, Ivar Heuch Multivariate meta-analysis for non-linear and other multi-parameter Department of Mathematics, University of Bergen, Bergen, Norway associations The rank correlation test introduced by Begg and Mazumdar (1994) is widely Antonio Gasparrini, Ben Armstrong, Michael G. Kenward used in meta-analysis to test for publication bias in clinical and epidemiological London School of Hygiene and Tropical Medicine, London, UK studies. It correlates the standardized treatment effect and the variance of the In this contribution we formalize the application of multivariate meta-analysis treatment effect. and meta-regression to synthesize estimates of multi-parameter associations 76/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info obtained in different studies. This modelling approach extends the standard two-stage analysis used to combine results across different sub-groups or populations, and can be applied in multi-site randomized controlled trials or observational studies including data from multiple locations. The most straightforward application is for the meta-analysis of non-linear relationships, described for example by regression coefficients of splines or other functions, but the methodology easily generalizes to any setting where complex associations are described by multiple correlated parameters. The modelling framework of multivariate meta-analysis is implemented in the package mvmeta within the statistical environment R. As an illustrative example, we propose a two-stage analysis for investigating the non-linear exposureresponse relationship between temperature and non-accidental mortality using time series data from multiple cities. Multivariate meta-analysis represents a useful analytical tool for studying complex associations through a two-stage procedure. an overall risk score based on multiple studies. In a final model there were 13 independent predictors of mortality including demographic attributes, disease status, biomarkers, prior medical history and medication usage. Classifying patients into deciles of risk produced a marked gradient in 3-year % mortality rate, from 8% in bottom decile to 71% in top decile. This study provides a template as to how multiple studies can be meaningfully combined to provide a generalisable risk score. J Dobson1, S.J. Pocock1, C Ariti1, K Poppe2, R.N. Doughty2 1 London School of Hygiene and Tropical Medicine, London, UK, 2University of Auckland, Auckland, New Zealand It is hypothesized that certain alleles can have a protective effect not only when inherited by the offspring but also as non-inherited maternal antigens (NIMA). To estimate the NIMA effect, large samples of families are needed. When large samples are not available, we propose a combined approach to estimate the NIMA effect from ascertained nuclear families and twin pairs. We develop a likelihood-based approach allowing for several ascertainment schemes, to accommodate for the outcome-dependent sampling scheme, and a familyspecific random term, to take into account the correlation between family members. We estimate the parameters using maximum likelihood based on the combined joint likelihood (CJL) approach. Our method has two main advantages over the existing methods. First, the joint likelihood approach, which models the joint genotype and phenotype distribution, can be more efficient than the prospective likelihood (PL), used in existing methods, for estimating the genetic odds ratios. Secondly, by using twins, as compared to case-controls used in existing methods, we can infer more accurately their parental genotypes, needed to estimate indirect effects, by assuming Mendelian transmission and random mating. Simulations show that the CJL is more efficient for estimating the NIMA odds ratios as compared to a familiesonly approach. To illustrate our approach, we use data from a family and a twin study from the National Repository of Family Material of the Arthritis and Rheumatism Council, and confirmed the protective NIMA effect, with an odds ratio of 0.477 (95% C.I. 0.264-0.864). C34.5 Combining Family and Twin Data in Association Studies to Estimate the Noninherited Maternal Antigens Effect Brunilda Balliu1, Roula Tsonaka1, Diane van der Woude2, Jane Worthington3, Ann Morgan4, Stefan Boehringer1, Jeanine J. Houwing-Duistermaat1 1 Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands, 2Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands, 3Arthritis Research C34.4 Epidemiology Unit, University of Manchester, Manchester, UK, Developing a predictive risk model for mortality from multiple cohort studies: an Campaign 4 Leeds Institute of Molecular Medicine, University of Leeds, Leeds, UK example in heart failure Developing risk scores based on combining data from multiple studies requires dealing with a number of methodological issues including 1) complex patterns of missing data and 2) heterogeneity among studies in terms of overall patient risk and differences in risk prediction. This talk shows the statistical analysis of data from the MAGGIC meta-analysis which incorporates patient-level data from 30 studies, both observational registries and RCTs, totaling 39372 patients with heart failure and over 20 baseline variables and subsequent mortality. This dataset has complex missing value problems including different amounts of missing data including studies where variables are completely missing. The model was developed using multivariable piecewise Poisson regression models with stepwise variable selection. Shared frailty models were used as an alternative method to fixed effects to model study heterogeneity. Multiple imputation using chained equations was used to impute missing values of covariates including the implementation of recent developments to allow for time to event data and interactions to be correctly modeled. The model developed captured the multifactorial influences on mortality risk in ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info 77/156 Thursday, 23 august - Mini-symposia MS1 Mini-symposium on Registerbased Epidemiology Cancer Registry of Norway and Institute of Basic Medical Sciences, University of Oslo, Norway Cancer is becoming an increasingly complex disease. New biomarkers are identified that not only predict prognosis, but also suggest different etiological pathways for various cancer subtypes. Breast cancer is one example where there is mounting evidence that risk factors for the disease vary by subtype. This rapid molecular development offers many new challenges to cancer registries. For new biomarker information to be useful the laboratories that report to the registry must conduct similar assays, quality control procedures need to be adequate, and the results must be reported in a standardized and uniform manner. The cancer registries that record this information must pay attention to new developments in the molecular arena, be flexible enough to include new markers as they emerge, yet require sufficient stringent proof that these markers are worthwhile before including them in the database. It is clear that detailed data with up-to-date molecular markers would benefit cancer epidemiologists and basic scientists trying to understand the etiology of disease or specific mechanisms in cancer subtypes. Clinicians should, in their clinical care, appreciate ardent requirements on their laboratories for quality control and systematic reporting of results on all new markers. Further, detailed, high-quality and systematic recording of various markers in cancer registries open up possibilities for large scale clinical trials or observational studies of the role of cancer medications in a real-life setting. Given the demand for rapid approvals of new cancer medications that have not been tested outside of clinical trials, this opens the possibilities for international multisite evaluation of effects of advanced cancer therapy. MS1.1 A woman’s reproductive history is related to diseases later in life Rolv Skjærven Department of Public Health and Primary Health Care, University of Bergen, Norway Studies have shown that there is a relation between preterm birth, stillbirth, fetal growth restriction and preeclampsia on the risk of cardiovascular diseases later in life for women. Thus pregnancy represents a unique opportunity to identify women who may be at increased risk for serious disease and early death. Adverse events in reproduction, especially stillbirths, are most often compensated by additional pregnancies. In Norway, most women (85%) who have a first pregnancy, will have a second pregnancy, and even more so if the first pregnancy ends with a perinatal death. Studies on long term health consequences related to pregnancy outcome have so far focused on pregnancy outcome of first pregnancy and not evaluated modifying effects of later reproduction. We find that negative effects of adverse events during pregnancy on long term health are reduced by the presence of successive pregnancies. We therefore suggest that studies on health related to pregnancy outcomes are based on the women’s complete reproduction, rather than only the outcome of first pregnancy. Our studies are based on The Medical Birth Registry of Norway, covering 43 MS1.4 years (1967-2009), linked to maternal (and paternal) death, and to the registry Double delights through twin registry research of education. Successive pregnancies to a woman are organized into Nancy L. Pedersen reproductive histories through linkage. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Sweden MS1.2 Studies of twins were long regarded as the epitome of an epidemiological What can be achieved with a good population-based cancer registry? design that could address the role of “environmental” risk factors without the Timo Hakulinen confounding of genetic or other familial factors. Indeed, twin studies have Finnish Cancer Registry, Institute for Statistical and Epidemiological Cancer allowed researchers to address a number of questions concerning the role of purported environmental risk factors, and at the same time evaluate whether Research, Finland A good cancer registry is an active institution for cancer research basing its these effects reflect familial and genetic confounding. During the 1980’s – activity on a cancer register- a database of all cancer cases occurring in a 1990’s, twin study designs were widely used to estimate heritability (in the defined population. Its activities encompass the estimation and prediction of absence of measured genotypes). Despite being genetically informative, twin cancer burden, uncovering the causes of cancer, contributing towards cancer studies have been less popular as a design of choice for studying genetic prevention, early detection, and evaluation on the outcome -both in survival linkage or association. Nevertheless, twin studies may be particularly efficient and in a broader sense- of patients in that defined population. Its data and for evaluating potential gene – by – environment interactions (and correlations), analyses are also used in resource planning and evaluation of various especially when the outcome is a continuously distributed measure. The programmes and functions of different organisations. They thus provide the double delights of twin registry research will be described with examples basis of cancer policy and foundations for cost estimates. The target groups of covering a variety of phenotypes, predominantly those within aging and these activities are the mankind, society, population, authorities, patients and psychiatry. scientists. In order to achieve all of this, guarantees in legislature and funding are needed, as well as a good coexistence of registration and the scientific use MS1.5 of the data. The Finnish Cancer Registry that has been in existence for 60 Register-based research on the epidemiology of aging years is used as an example to elucidate these principles and issues in Kaare Christensen practice. The Danish Aging Research Center and the Danish Twin Registry, Denmark The Nordic countries have a long tradition for register-based epidemiological MS1.3 studies on reproduction and diseases taking into account socioeconomic How can cancer registries improve our biological understanding of factors. Mainly due to truncation, register-based research on the epidemiology cancer and cancer care? of aging has been less extensive. A combination of existing disease- specific Giske Ursin registers and national health registers and demographic databases in Denmark 78/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info have opened up new possibilities. Examples of recent uses addressing the through assisted reproductive techniques or ART). Infertile couples’ underlying male-female health survival paradox, the cancer-longevity trade-off, and the risk can make ART itself appear harmful. Some of the best evidence for such change of hospitalization use within and across cohorts of the oldest old risk heterogeneity (and the misleading distortions that can result) comes from citizens in Denmark will be provided to illustrate some of the potentials. the linked Scandinavian birth registries. Examples will be discussed. MS1.6 The Birth Register – how do we find the most beautiful flowers in the garden? Sven Cnattingius Department of Medicine, Clinical Epidemiology Unit, Karolinska Institutet, Sweden The success of the Scandinavian birth registers is to some extent due to the specific characteristics of pregnancy and childbirth. The window of exposure is short (9 months), and data are commonly prospectively collected. In contrast to chronic diseases, pregnancies often occur more than once. This makes it not only possible to study risks of recurrent or isolated events, but also to study if change of exposure from one pregnancy to another (i.e., change in smoking habits, weight gain or change of partner) influences risks. The Birth Registers also include information on both exposures and outcomes, which makes it possible to perform analytic studies without other data sources. As the Birth Registers in Norway and Sweden started in 1967 and 1973, respectively, it is possible to perform studies of birth outcomes across generations, and further information about family relationships (siblings, half siblings on maternal or paternal side, etc.) provides additional possibilities to study hereditary patterns. In studies of long-term effects of prenatal exposures, studies within diseasediscordant sibling pairs provide control for otherwise unmeasured familial (shared genetic and environmental) factors. Limitations in the Birth Registers include no direct access to stored biological samples, lack of information on early pregnancy losses, and limited information on prenatal diagnostic procedures and the delivery process. In future, these limitations may partly be overcome by adding information from computer-based standardized antenatal and obstetrical records. However, given the short time of exposure, pregnancy will always be well suited for prospective cohort studies with more detailed information. MS1.7 Heterogeneity of risk and selective fertility – Subtle biases produce serious confusions Allen J. Wilcox1 and Rolv Skjærven2 1 National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA and 2Department of Public Health and Primary Health Care, University of Bergen, Norway Measures of perinatal risk (neonatal mortality, for example) may appear relatively steady for given populations, but in fact they summarize a mix of people at highly heterogeneous risks. While such heterogeneity is usually invisible, it can easily be confused with real biological effect when it becomes visible. This can happen when there is selective over- or under-representation of high-risk couples. For example, women who suffer a pregnancy loss often have additional pregnancies in order to achieve their desired family size, which leads to more high-risk women delivering at older ages. The overrepresentation of high-risk women at older maternal ages then produces an association that can be mistaken for a direct effect of maternal “aging.” High-risk people can also be underrepresented. Adults who have survived a perinatal problem (such as a birth defect) are usually at risk of passing on similar outcomes to their offspring -- but may also be less likely than other adults to contribute offspring to the next generation. Similarly, infertility is associated with a range of pregnancy problems, but infertile couples are less likely to contribute those risks to the pool of observed pregnancies – unless interventions are done to make such pregnancies possible (for example MS2 Mini-symposium on Statistics in Vaccines Research MS2.1 FDA’s Sentinel Initiative: Active Vaccine Safety Surveillance and Pharmacovigilance Michael Nguyen LCDR, U.S. Public Health Service, Acting Chief, Vaccine Safety Branch, FDA Center for Biologics Evaluation and Research, USA The ability to monitor the safety of vaccines after licensure is as important as the ability to evaluate and demonstrate their safety before licensure. In 2008, the United States Food and Drug Administration (FDA) launched the Sentinel Initiative to expand its capability to routinely, rapidly and continually monitor a product’s benefit-risk balance postlicensure. Capitalizing on the growing availability of electronic health data, the five year Mini-Sentinel pilot was launched to inform the creation of the fully operational Sentinel System, a national electronic postmarket risk identification system. As of 2012, MiniSentinel has created a distributed database of more than 126 million individuals and has already enabled FDA to conduct rapid, population-based risk assessments of FDA regulated medical products. The Postlicensure Rapid Immunization Safety Monitoring program (PRISM) is the Mini-Sentinel program dedicated to vaccine safety. PRISM is the largest active surveillance vaccine safety system worldwide and uniquely strengthened by linkages to immunization registries. PRISM is currently focused on developing new statistical and epidemiologic methods to detect, quantify and characterize vaccine safety concerns in near real-time. This presentation will discuss the PRISM program’s current status as well as progress towards integrating PRISM into FDA’s vaccine regulation processes. MS2.2 Methodological challenges for sequential vaccine safety surveillance using observational health care data Jennifer C. Nelson1,2, Andrea Cook1,2, Onchee Yu1, Lisa Jackson 1,3 1 Group Health Research Institute, 2Department of Biostatistics, University of Washington, 3Departments of Epidemiology and Medicine, University of Washington, USA In order to improve post-licensure vaccine safety surveillance, new systems have been developed that prospectively monitor observational health care data from large health plans. Such systems, which include the Centers for Disease Control and Prevention’s Vaccine Safety Datalink project and the Food and Drug Administration’s Sentinel System, involve capturing and prospectively analyzing vaccine and adverse event data among enrollees of multiple large health plans as new vaccines are received (and often in near-real time). Their goal has been to evaluate pre-specified suspected safety issues and potentially prompt a more formal confirmatory study. They offer promise to provide a safety monitoring framework that is rapid, statistically powerful, and costeffective. Continuous sequential testing has been proposed in this setting to facilitate the rapid detection of increased risks of adverse events for newly licensed vaccines. Group sequential methods, commonly used in randomized clinical trials, have also been considered. In this talk, we describe the key methodological challenges that arise when applying sequential methods to observational safety surveillance that uses data from large health plans. ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info 79/156 Challenges primarily derive from the lack of a controlled experiment and MS2.5 include confounding, outcome misclassification, and unpredictable changes in Paediatric vaccine pharmacoepidemiology : classification bias in case the data over time, such as differential vaccine uptake. series analysis and application to febrile convulsions C. P. Farrington1, C. Quantin2, E. Benzenine2, M. Velten3, F. Huet4, P. TubertMS2.3 Bitter5 Drug and Vaccine Safety Surveillance: some existing methods and the 1Department of Mathematics and Statistics, Open University, Milton Keynes, unmet analytic needs UK, 2Départment de l’Information Médicale, CHU Dijon, Université de Bourgogne, France, 3 Laboratoire d’Epidémiologie et de Santé Publique, Lingling Li Université de Strasbourg, France, 4 Pôle Pédiatrie, CHU Dijon, Université de Department of Population Medicine, Harvard Medical School and Harvard Bourgogne, France, 5 Equipe Biostatistique, Inserm-UPS UMRS 1018, Villejuif, Pilgrim Health Care Institute, USA France The importance of post-marketing surveillance using electronic healthcare The self-controlled case series (SCCS) method was developed in order to databases for drug and vaccine safety is well recognized as rare but serious study adverse events of vaccines and was originally applied to hospital adverse events (AE) may not be detected in pre-approval clinical trials. In such discharge data linked to vaccination data in the UK. surveillance, a sequential test is preferable, in order to detect potential problems as soon as possible. In this talk, we will briefly introduce several In the first part of the talk, an ongoing project to link data on cases and sequential analytic methods that have been developed for this purpose, i.e., vaccinations in France will be described. The objective of this study is to the Poisson and Binomial maximized sequential probability ratio (MaxSPRT) assess the validity of identifying hospitalizations for simple febrile convulsion tests, the conditional maximized sequential probability ratio test (CMaxSPRT), from discharge summaries recorded in the French hospital automated the conditional sequential sampling procedure (CSSP), and the propensity database system. Preliminary data from case reviews of 451 children aged score(PS)-enhanced CSSP test. The Poisson MaxSPRT and CMaxSPRT are between 29 days and 36 months from four hospitals in the Bas-Rhin and the both extensions of Wald’s classical SPRT, and apply to settings with historical Côte-d’Or indicate that the French hospital automated databases can be used controls. The Poisson MaxSPRT requires rich historical data to provide stable effectively to identify cases of hospitalization for febrile convulsion. estimates of the baseline AE counts, while the CMaxSPRT adjusts for In the second part of the talk, the implications of these results for SCCS uncertainty in both historical controls and surveillance population. The studies, and potential bias from different methods of case ascertainment, Binomial MaxSPRT and its variants apply to settings with matched con-current including purely automated data extraction, will be discussed. controls. The CSSP and the PS-enhanced CSSP apply to settings with con- More generally, the impact of classification biases on the SCCS method will be current controls, but do not require matching. The CSSP adjusts for assessed, in the specific context of data linkage studies. The relative impact of confounding by standard stratification while the PS-enhanced CSSP adjusts for sensitivity and specificity of case ascertainment and the accuracy of confounding by PS stratification. We will discuss the respective advantages vaccination data on the bias and the power of the method will be described. and disadvantages of these tests. More importantly, in this talk, we will discuss the remaining knowledge gaps and unmet analytic needs for post-marketing drug and vaccine safety surveillance to motivate more methodology research MS2.6 to benefit vaccine safety research. Self controlled case series method with smooth age effect Yonas G. Weldeselassie, Heather Whitaker, Paddy Farrington MS2.4 The Open University, Walton hall, Milton Keynes, MK7 6AA, UK Signal Detection of Adverse Events Using Electronic Data with Outcome The self-controlled case-series method, commonly used to investigate potential Misclassification associations between vaccines and adverse events, requires information on cases only and automatically controls all age-independent multiplicative Stanley Xu confounders, while allowing for an age dependent baseline incidence. Institute for Health Research, Kaiser Colorado, USA In the parametric version of the method, the age specific relative incidence is Availability of large amount of electronic health care data makes it possible to modelled using piecewise constant functions, while in the semiparametric study the association of rare adverse events with certain vaccines. For version it is left unspecified. However, misspecification of age groups in the example, the Vaccine Safety Datalink (VSD) project was established a decade parametric version leads to biased estimates of the vaccine effect, and the ago in USA. It collects electronic data including vaccinations and medically semiparametric approach runs into computational problems when the number attended adverse events on 8.8 million managed care organizations (MCO) of cases in the study is large. We propose to use a penalized likelihood enrollees annually. However, vaccine safety studies using these electronic approach where age effect is modelled using splines, piecewise polynomial data bases are limited by the quality of the MCO databases, which are not functions that are combined linearly to approximate a function on an interval originally created for research purposes. While the automated vaccination data with specified continuity constraints. are of high quality, studies have demonstrated that the accuracy of the outcome data is often inadequate. In this study, we demonstrated that outcome We use M-splines to approximate the age specific relative incidence and misclassification could result in both false positive and false negative signals in integrated splines (I-splines) for the cumulative relative incidence. A simulation screening studies and near-real time surveillances. We developed a joint study was conducted to evaluate the performance of the new approach and its statistical model that accommodated misclassification of adverse events for efficiency relative to the semiparametric approach. Results show that the new both cohort and self-controlled case series designs. The joint statistical model approach performs better and works well for large data sets. The new splineconsisted of two components: an incidence rate model for the observed count based approach will be applied to data on febrile convulsions and paediatric of adverse events and a misclassification model for modeling the likelihood of vaccines. misclassification of observed adverse events. Simulation studies showed that Key words: Case series, Penalized likelihood, Poisson process, the newly proposed model reduced the rates of false positive and false Semiparametric model, Smoothing,Ssplines, negative signals in vaccine safety studies. 80/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info Abstracts - Posters Chia-Min Chen2, Yunchan Chi1 1 Department of Statistics, National Cheng-Kung University, Tainan, Taiwan, P1.1 2 Graduate Institute of Natural Healing Sciences, Nanhua University, Chiayi, Response Adaptive Randomization - Cost, Benefit and Implementation with Taiwan Covariate Balancing In a randomized two arms phase II clinical trial, the goal is to determine Wenle Zhao, Yuko Palesch whether the superior therapy is better than the inferior therapy. Most often, the Medical University of South Carolina, Charleston, SC, USA trial uses a two-stage design for ethical considerations. The sample size Response adaptive randomization (RAR) in clinical trials has been advocated required in the trial is often determined by the expected difference in response for its presumed benefits in ethical considerations and statistical efficiencies. rates between the two therapies based on desired power. In practice, however, However, we found that the potential benefit in statistical efficiency is trivial, it is difficult to know the expected difference, especially when there is no and the ethical benefit may be associated with a loss in test power. available information about the two therapies in the literature. Therefore, this Furthermore, consideration for baseline covariate balance in randomization via paper follows the idea of Lin and Shih (2004) to develop an adaptive two-stage stratification makes the implementation of RAR even more complex and less design which allows the expected difference in the alternative hypothesis at the effective. To overcome this drawback, we propose a RAR algorithm with a second stage to be changed according to the outcome observed at the first minimal sufficient balancing for important baseline covariates and an stage. Consequently, a promising therapy may be rejected based on the adjustable response adaptation based on clinical considerations. We adapted alternative hypothesis. The Fisher's exact test is employed and its demonstrate that prevention of serious imbalances in important baseline exact distribution is used for generating sample sizes required the design. covariates is superior to the simple RAR with respect to statistical operating Because of the discrete nature of the exact distribution, the Mid-P-value is characteristics. The covariate-adjusted RAR algorithm aims to achieve a target applied to overcome conservativeness of Fisher's exact test. allocation ratio which is sufficient to demonstrate ethical advantage and also is capped in order to contain the power loss of the final statistical analysis. The P1.4 operation characteristics and statistical properties of this RAR are studied with Impact of lack-of-benefit stopping rules on treatment effect estimates of twocomputer simulation. This design has been implemented in a large multicenter arm multi-stage (TAMS) trials with time to event outcome acute ischemic stroke trial funded by NIH. Babak Choodari-Oskooei1, Mahesh KB Parmar1, Patrick Royston1, Jack Bowden2 P1.2 1 MRC Clinical Trials Unit, London, UK, 2MRC Biostatistics Unit, Cambridge, UK Maximum type 1 error rate inflation in multi-armed clinical trials with interim Background sample size modifications In 2011, Royston et al described technical details of a two-arm, multi-stage Alexandra Graf, Peter Bauer, Franz Koenig (TAMS) design. The design enables a trial to be stopped part-way through Medical University Vienna, Vienna, Austria recruitment if the accumulating data suggests a lack of benefit of the Sample size modifications in an adaptive interim analysis based on the experimental arm. Crucially, such interim decisions can be made using data on observed interim effects can considerably inflate the type 1 error rate if the pre- an available `intermediate' outcome. At the conclusion of the trial, the definitive planned conventional fixed sample-size tests are applied in the final analysis, outcome is analysed. Typical intermediate and definitive outcomes in cancer ignoring the adaptive character of the study. We investigate scenarios where trials might be progression-free and overall survival, respectively. Despite this more than one treatment arms are compared to a single control as well as framework being used in practice, concern has been raised about possible bias scenarios with interim treatment selection by carrying on only the treatment induced in the final estimate of the treatment effect. with the largest observed interim effect and the control to the second stage. It Methods is assumed that either a naive testing procedure with a conventional fixed sample-size test or a multiplicity adjusted Dunnett test is performed in the final We explore the issue of bias empirically through simulation and bootstrapanalysis. The maximum inflation of the type 1 error rate for such types of based reanalyses of cancer trials run by the Medical Research Council. design can be calculated by searching for "worst case" scenarios, i.e. sample Results size adaptation rules that lead to the largest conditional type 1 error rate in any In trials with a true lack of benefit of the experimental arm that are stopped at point of the sample space. an interim stage, the treatment effect has a small bias at the time of the interim If allocation rates to treatment arms are modified after an interim analysis, it assessment. This small bias is markedly reduced by further follow-up and can be shown that the maximum inflation of the type 1 error rate may be reanalysis at the planned end of the trial. In trials with a truly efficacious substantially larger than in the case of sample size reassessment with stage- experimental arm that continue to the planned end, the bias is of no practical wise balanced sample sizes. To achieve the maximum type 1 error rate, we importance, being less than 3% of the treatment effect in general. first assume unconstrained second-stage-sample-sizes. To see how the Conclusions numbers will change in more realistic scenarios, we put constraints on the The bias in the estimated treatment effect in a TAMS trial is of no practical second-stage-sample-size, which may lead to scenarios not inflating the type 1 importance, provided that all patients are followed up to the planned end of the error rate. trial. Bias correction is unnecessary. P1 Adaptive clinical trials P1.3 P1.5 Adaptive two-stage designs for comparing two binomial proportions in phase II Optimizing trial design in pharmacogenetics research; comparing a fixed clinical trials ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info parallel group, group sequential and adaptive selection design on sample size requirements Ruud Boessen1, Frederieke H van der Baan1, Rolf HH Groenwold1, Antoine CG Egberts3, Olaf H Klungel2, Diederick E Grobbee1, Mirjam J Knol1, Kit CB Roes1 1 Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands, 2Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht University, Utrecht, The Netherlands, 3 Department of Clinical Pharmacy, University Medical Center Utrecht, Utrecht, The Netherlands Background: Two-stage clinical trial designs may be efficient in pharmacogenetics research when there is inconclusive evidence of effect modification by a genomic marker. Two-stage designs allow to stop early for efficacy/futility (i.e. group sequential), and could offer the additional opportunity to enrich the study population to the most promising patient subgroup (i.e. adaptive selection). Methods: This study compared sample size requirements for a fixed parallel group, a group sequential and an adaptive selection design with equal overall power and control of the family-wise type-I error rate. Designs were evaluated across scenarios that defined the effect size in the marker positive and marker negative subgroups, and the genotype distribution in the total study population. Also considered were scenarios where the actual subgroup effects were different from those assumed at the planning stage. Results: Two-stage designs were generally more efficient than the fixed parallel group design. The largest sample size reduction was associated with the option to stop early for efficacy/ futility. The possibility to enrich had an additional advantage when the difference in subgroup effects was large. When the actual difference in subgroup effects was larger than assumed, an adaptive selection trial more often concluded significance only for the most responsive subgroup. Conclusion: A group sequential design is generally more efficient than a parallel group design when patient subgroups respond differentially. An adaptive selection design only adds to the advantage when the difference in subgroup effects is large. Adaptive selection provides flexibility which may be desirable when a priori assumptions are tentative. P1.6 In a two-stage dose-finding study, how big should the first stage be? Emma McCallum, Adrian Mander, James Wason MRC Biostatistics Unit, Cambridge, UK In Phase II of the drug development process, trials are used to establish the efficacy of a drug and to estimate a dose-response relationship. Response is usually an efficacy endpoint such as blood pressure or surrogate marker such as white blood cell count. When the aim is to estimate the full dose-response relationship, nonlinear monotonic response models, such as the Emax model, are often used to estimate clinical parameters. Adaptive optimal designs split a trial into multiple stages; at each stage, parameters of the model are estimated and future dose choices are based on a locally D-optimal design. The main problem with these designs is: how big should the first stage be when there is no information about the model parameters? We have examined a two-stage design that is partitioned according to some function. In the first stage, participants are allocated to a set of pre-defined doses in order to estimate the parameter values of the doseresponse curve. These parameter estimates are then used to find the optimal design for the second stage. We shall examine what fraction of the participants should be assigned to the first stage so that the efficiency of the design is maximised and how this is affected by the initial guesses of the model parameters. The problem is explored in the context of a one parameter exponential mean function and also a multi-parameter Emax model. With these models, some analytic results are obtained about how big the first stage of 81/156 these trials should be. P1.7 Incorporating prior information into dual-agent Phase I dose-escalation studies from single-agent trials Graham Wheeler MRC Biostatistics Unit, Cambridge, UK In oncology, there is increasing interest in studying combinations of drugs to improve treatment efficacy and/or reduce harmful side-effects. Dual-agent Phase I clinical trials are primarily concerned with drug safety, with the aim to discover a maximum tolerated combination dose via dose-escalation; small cohorts of patients are given set doses of both drugs and monitored to see if any particular toxic reactions occur. Whether to escalate, de-escalate or maintain the current dose for either drug for subsequent cohorts is based on the number and severity of observed toxic reactions, and a decision rule. We investigate the use of Bayesian adaptive model-based designs for dualagent Phase I trials, where prior information from single-agent trials can be accommodated. We use a meta-analytic method to incorporate information from past single-agent trials in order to construct prior predictive distributions for the margins of the dose-toxicity surface. Priors for parameters relating to drug-drug interactions are initially kept vague. These methods are used to design a dual-agent dose-escalation study in pancreatic cancer; the intervention is a combination of Paclitaxel and a novel Aurora Kinase Inhibitor. In using data obtained from a systematic search of Paclitaxel toxicities we construct an appropriate prior distribution for one margin of the dose-toxicity surface. We then consider several prior distributions for the other margin and interaction terms under various scenarios and show how they affect the operating characteristics of the design. P1.8 Blinded and unblinded internal pilot study designs for clinical trials with overdispersed count data Simon Schneider1, Heinz Schmidli2, Tim Friede1 1 Department of Medical Statistics, Göttingen, Germany, 2Statistical Methodology, Novartis Pharma AG, Basel, Switzerland In the planning phase of a clinical trial with counts as primary outcomes, such as relapses in Multiple Sclerosis (MS), there is uncertainty with regard to the nuisance parameters (e.g. overall event rate, the dispersion parameter) which need to be specified for sample size estimation. For this reason the application of adaptive designs with blinded sample size reestimation (BSSR) are attractive (Cook et al. 2009, Friede and Schmidli 2010a). After a comparison of existing methods we consider in this presentation a modified version of the maximum likelihood method for BSSR for negative binomial data proposed by Friede and Schmidli (2010b). The method works well in terms of sample size distribution and power, if the assumed clinically effect is equal to the true effect. We compare the BSSR approach to an unblinded procedure in situations where an uncertainty about the assumed effect size exists. For practically relevant scenarios we make recommendations when application of the blinded or unblinded procedures are indicated. In addition, results for unbalanced designs previously not considered are shown in a simulation study. The methods are illustrated by a study in Relapsing Remitting MS. Cook RJ et al.. Two-stage design of clinical trials involving recurrent events. Statistics in Medicine 2009; 28: 2617-2638. Friede T, Schmidli H. Blinded sample size reestimation with count data: Methods and applications in multiple sclerosis. Statistics in Medicine 2010a; 29: 1145-1156. Friede T, Schmidli H. Blinded Sample Size Reestimation with Negative 82/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info Binomial Counts in Superiority and Non-inferiority Trials. Methods of information across samples from the same gene. We apply the model to Information in Medicine. 2010b; 49: 618-624. simulated data and a mouse RNA-Seq data. Empirical tests show that our model provides substantial improvement on the quality of model fitting and improves the sensitivity in isoform-level differential expression analysis when P1.9 read distribution deviates from uniform. Some of the expression levels are Group sequential testing in covariate-adjusted response-adaptive designs validated using real-time PCR data. Eunsik Park Chonnam National University, Gwangju, Republic of Korea P2.2 Unequal group covariances in microarray data analyses 1 2 Response adaptive design in clinical trial is to minimize the number of subjects Woojoo Lee , Yudi Pawitan 1 assigned to the inferior treatment while maintaining significant statistical Inha university, Incheon, Republic of Korea, 2Karolinska Institutet, Stockholm, inference at certain level. Recently, the molecular studies, and the genetic and Sweden proteomic researches cumulates more and more evidences which suggest the personalized medicine is possible. Toward this end, the clinical design to In testing for differential expression for gene sets, such as genetic pathways, incorporate the information of covariates becomes important and ethical. In this we often face the problem of singular covariance matrices (if n< p) and of paper, we study the application of group sequential methods to some covariate unequal covariance matrices between groups. To deal with this singularity problem, we can apply a shrinkage covariance estimation method and this adjusted and response adaptive design. leads to a regularized version of Hotelling's T2. However, the Welch-type The group sequential methods with covariate adjustment have been studied by regularized Hotelling's T2 lacks sensitivity when the covariances are equal. To many authors (for example, Jennison and Turnbull, 1997). Recently, in the overcome this problem, we introduce a moderated regularized Hotelling's T2 literature, there are some discussions about application of group sequential that is based on weighing by the probability that the covariances are equal methods to response adaptive design (for example, Karrison, Huo, Chappell, given data. When a non-trivial proportion of gene sets has unequal covariance Control Clinical Trials, 2003, pp 506-22). When the response adaptive design is matrices, the false discovery rate (FDR) estimate based on the proposed applied, the subjects included to study are adaptive to the previous history of statistic is shown to perform better than the existing methods over wider range response and therefore they are no longer independent. That is, the group of data conditions. sequential method is built on the theory of Jennison and Turnbull (1997, 2000) which relies on the asymptotic theory. P3 Causal inference By basing treatment allocation to a better treatment, participation of patients to clinical trials will be improved in serious diseases. This will activate researches P3.1 in serious diseases. Exploration of instrumental variable methods for estimation of causal mediation Research period can be shortened by stopping the study earlier when stopping effects in the PACE trial of complex treatments for chronic fatigue syndrome criteria are satisfied. This will reduce study costs and time to access to the Kimberley Goldsmith1, Trudie Chalder1, Peter White2, Michael Sharpe3, Andrew better treatment will be saved. Pickles1 1 Institute of Psychiatry, King's College London, London, UK, 2Wolfson Institute P2 Bioinformatics of Preventive Medicine, Bart's and the London School of Medicine, Queen Mary University of London, London, UK, 3University Department of Psychiatry, P2.1 Joint estimation of isoform expression and isoform-specific read distribution University of Oxford, Oxford, UK Background using RNA-Seq data across samples Chronic fatigue syndrome (CFS) is characterised by chronic disabling fatigue. Chen Suo1, Stefano Calza2, Agus Salim3, Yudi Pawitan1 1 Department of Medical Epidemiology, Karolinska Institutet, Stockholm, The PACE trial compared four treatments for CFS and found cognitive Sweden, 2Department of Biomedical Sciences and Biotechnology, University of behaviour therapy (plus specialist medical care, CBT+SMC) and graded Brescia, Brescia, Italy, 3Department of Epidemiology and Public Health, exercise therapy (GET+SMC) to be more effective than adaptive pacing therapy (APT+SMC) and SMC alone in improving physical function and fatigue. National University of Singapore, Singapore, Singapore Estimates of causal mediation effects are of interest, for example, fear RNA-sequencing technologies provide a powerful tool for expression analysis avoidance and activity avoidance as mediators of the effect of CBT and GET at isoform level, but accurate estimation of isoform abundance is still a respectively. Traditional Baron, Judd and Kenny (BJK) methods can be subject challenge. The first step of transcript quantification is to count the number of to bias; instrumental variable methods (IV) can address this problem. The aims reads falling into an exon because expression level is expected to be were to explore causal analyses using IVs in PACE and to compare IV and proportional to the read counts. Standard methods of estimation typically BJK estimates. assume uniform read intensity along a transcript; these methods would Methods produce biased estimates when the read intensity is in fact non-uniform due to, BJK methods were applied using ordinary least squares regressions. IV for example, the 5' or 3' bias, certain nucleotide composition effect - such as methods were applied by assessing several baseline variables in interaction GC content - or other technical biases. The problem is that the read intensity terms with treatment arm. Instrument strength was assessed using the R2 pattern is not identifiable from data observed in a single sample. In this study, change between models with main effects only and with the interaction term. we propose a joint statistical model that accounts for non-uniform isoformDifferent IV estimators were compared. Collective instrument strength was specific read distribution and gene isoform expression estimation. The main assessed using an F test and partial R2. challenge is in dealing with the large number of isoform-specific read distributions, which are as many as the number of splice variants in the Results genome. A statistical regularization with L1 smoothing penalty is imposed to The IVs were weak, with small R2 changes. The IV-derived estimators were control the estimation. Also, for estimability reasons, the method uses different in magnitude and less precise than the BJK estimators. The relative ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info precision of different IV estimators varied 10-18%. There is scope for modelling a common effect of mediators on outcomes across trial arms. Conclusions Potential IVs for the study of PACE treatment mechanisms can be found, however, these were weak. Combining trial arms may allow for more efficient IV analysis. 83/156 mediator. The latter is modifiable at the design stage. We extended the twostage approach, using linear mixed effects and GEE modelling to enable multiple SF36 outcomes to be modelled multivariately, thereby contributing more precision to estimate a common mediation effect, and achieving reductions of 20-30% in the standard error. This offers welcome power gains. In the trial, the significant mediation effect through sessions indicates that some of the effect may indeed be genuinely connected with receipt of intervention material. P3.2 Principal trajectories: extending principal stratification for repeated measures P3.4 Richard Emsley, Graham Dunn, PRP (Psychosis Research Partnership) Group Health Sciences Research Group, The University of Manchester, Manchester, Estimating the effect of insulin treatment in diabetic type-II patients on cardiovascular disease rates with marginal structural models UK Exploring treatment effect heterogeneity is an important aspect of complex Michel Hof, Aeilko Zwinderman intervention trials, and process variables describing the intervention content are Academic Medical Center, Amsterdam, The Netherlands crucial components of this. Frequently these variables can only be measured in The first pharmacological treatment of diabetes mellitus (DM) type-II is intervention groups. Principal stratification, whereby control group participants commonly a variety of oral anti-diabetics. However, at some point in their are assigned to the latent class they would have been in had they been treatment, most DM type-II patients require exogenous insulin to control randomised to intervention, has been proposed for this setting but generally glucose-metabolism. Unfortunately, there are suspicions that the use of makes use of a single observation of the process variable, which often has exogenous insulin is associated with increased risk of cardiovascular events repeated measures collected. independent of the risk for such events that is associated with having DM typeWe propose a new method making efficient use of all the observed data, II. termed principal trajectories. We estimate general growth mixture models on To investigate this hypothesis, baseline biomarker measurements and the repeated measures of process variables in the intervention group using complete medication histories from 25883 diabetic type-II patients were maximum likelihood, assigning participants to hypothesised latent trajectory available. This data was extracted from the PHARMO record linkage system classes by estimated posterior probabilities. Using baseline covariates which containing the data from Dutch community pharmacies. predict class membership, we assign which class control group participants would have been in, had they been randomised to intervention. We then In this epidemiological cohort the health of patients treated with exogenous examine the effect of random allocation on outcome within each class. If insulin is usually worse than the health of patients treated with oral antidiabetics. To account for this indication-bias we used marginal structural Cox required, an exclusion restriction can be imposed to aid identification. proportional hazards models to quantify the causal effect of starting with We illustrate this method using a randomised trial of cognitive behaviour exogenous insulin therapy during follow-up. Different types of models were therapy for prevention of relapse in psychosis. A participant-reported measure considered, based on (double-robust) inverse probability of treatment weighted of therapist empathy was taken at each session of therapy in the intervention estimating equations or targeted maximum likelihood. group, with no corresponding measure available in the control group. Applying the principal trajectories approach, we find differential effects of randomisation Preliminary results showed that the increased risk that is associated with starting exogenous insulin treatment is largely explained by the dynamic on several psychosis-specific outcomes between the latent classes. selection of patients who were started on insulin treatment. P3.3 Improving the detection of causal mediation effects in complex intervention trials Neil Casey1, Simon Thompson1, Toby Prevost2 1 University of Cambridge, Cambridge, UK, 2King's College London, London, UK P3.5 Causal inference from trials of complex interventions Sabine Landau King's College London, London, UK In a physical activity trial, there was no evidence of effect on the primary outcome, though large significant effects amongst eight related SF36 measures of general health. Could such differences be: true effects mediated through receiving the intervention, other systematic effects such as selfreporting bias, and/or chance? The aim was to develop reliable methods to investigate whether the intervention effects on these outcomes were mediated through truly receiving the intervention delivered in sessions. We adopted a structural mean modelling approach with a two-stage leastsquares estimation algorithm, to estimate mediation effects free from confounding bias. The mediator (number of sessions attended), and outcomes (SF36), are predicted from baseline covariates. Each individual has a personal predicted ‘counterfactual' treatment-effect difference, regressed on the predicted mediator using a dose-response model structure. However, these methods do not typically provide sufficiently precise estimates except for such a simple model structure. A simulation study established the factors driving the lack of precision to be the intervention effect size and the degree to which baseline covariates predict the Complex interventions are characterised by multiple components which when given in combination are thought to improve health outcome. Outcome improvements are often hypothesized to be achieved indirectly by an intervention component inducing change in an intermediate process variable which is then translated into change in a distal outcome (mediation). In clinical trials covariates of linear models employed to estimate average causal treatment component effects and their indirect (mediated) and direct (nonmediated) portions may be endogenous due to non-receipt of assigned intervention components, unmeasured confounding or measurement error. I will describe a Monte Carlo simulation study to assess the impact of these issues on the statistical properties of ordinary least squares (OLS) estimators and two instrumental variables (IV) estimators (two-stage least squares, 2SLS, three-stage least squares, 3SLS). The results show that while IV estimators can correct for hidden confounding and/or measurement error bias they do so at the expense of increased imprecision. The standard error inflation relative to that of the OLS estimator is largely driven by the strength of the instruments the stronger the instruments the less inflation. The mediation portions were found to be most susceptible to bias by naïve OLS estimation. At the same 84/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info time the standard errors of their IV estimators suffered the worst inflation. There was no efficiency gain from implementing the extra computations required by the 3SLS procedure. The findings highlight the need to design (series of) trials of complex interventions such that consistent and precise estimation of causal parameters of interest is possible. balanced exposure and three confounding factors. We simulated several datasets reproducing the original sample to compare the ATE estimation methods (STD, IPW and DR) in terms of bias, size of confidence interval and power to detect a non-zero effect. We also conceived new scenarios by varying the number of cases, the strength of the ATE and the prevalence of outcome among non-exposed subjects. In the original sample, the odds ratios among men and women were 0.89 P3.6 [0.33;2.36] and 4.44 [1.17;16.74] for DR, 0.87 [0.32;2.35] and 4.23 [1.16;15.38] Marginal Structural Models in Epidemiology: Why not? for IPW and 0.89 [0.34;2.33] and 4.45 [1.27;15.61] for STD, respectively. The simulations are currently running and their results will be presented at the Silvana Romio1, Maria Rosaria Galanti2, Maria Paola Caria2, Rino Bellocco3 1 Erasmus University Medical Center, Rotterdam, The Netherlands, 2Karolinska conference. In the case of a rare outcome, the estimation of the ATE is complex, and Institutet, Stockholm, Sweden, 3University of Milano-Bicocca, Milan, Italy simulations are needed to accurately study power and variance. Background: Although the study of the determinants of disease is a priority in epidemiology, causal models to study the relation between an exposure and an outcome are seldom used in epidemiologic studies. Case studies may be P3.8 important in order to understand advantages and limitations of such models G-estimation from an RCT comparing 2 active treatments and placebo given application. post-randomisation crossover and simultaneous treatments Objectives: To compare methods and results from studies where marginal Roseanne McNamee, Matthew Carr structural models have been applied, in order to highlight aspects of University of Manchester, Manchester, UK importance in the application of these models in epidemiology. Methods: Objectives, study design, causal graphs, and nature of confounding Background and objectives. In a 2-arm placebo-controlled randomised trial and feasibility of the application were compared in four studies based on (RCT), where cross-over from one arm to the other occurs non-randomly, gcausal models in different contexts. Main focus of this comparison concerned estimation provides an unbiased compliance-adjusted estimate of active model assumptions and strength of the relationship between confounder and treatment effect on time to event. In the 3-arm RCT with two anti-hypertensive arms and placebo which gave rise to this work, patients were allowed to switch exposure of interest. from one treatment to another, or receive both treatments simultaneously. Our Results: All studies compared classic and marginal structural models. objectives were to extend g-estimation to 3 arms, and to estimate the efficacy Substantial difference in the estimation of the effect of exposure on outcome of the active treatments (T1 and T2) on time to death, MI and stroke. was observed in only one of the considered works. The strength of the association between time-dependent confounders and exposure was a Method. A structural accelerated life time model - with a re-censoring rule based on an empirical minimum time (Joffe 2011) - was used to relate the peculiarity of this study. (sometimes) unobserved treatment-free event time (T 0) to observed event Conclusions: The application of marginal structural models in epidemiology times. Effects of active treatments were assumed to act multiplicatively if given requires further exploration. Assumptions to be fulfilled and construction of together, and the placebo to have no effect; therefore the model had two causal diagrams are crucial aspects of this exploration, as well as of wider parameters. Parameter estimates were found as the values for which, in a potential application of the models in epidemiologic studies. three arm comparison a two degrees of freedom log-rank test of the null hypothesis of no difference in distribution of T 0, yields p=1. Issues around confidence interval estimation, competing risks and run-time will also be Average treatment effect estimation with a rare binary outcome: an example discussed. Results: Estimated acceleration factors were converted to Hazard Ratios (HR). and simulations Whereas the ITT HRs for mortality, for example, under for T1 and T2 were 0.86 1 2 3 2 Eléonore Herquelot , Julie Bodin , Catherine Ha , Yves Roquelaure , Rémi and 1.07, the g-estimation efficacy estimates were 0.71 (95% CI: 0.49, 1.11) Sitta1, Alice Guéguen1, Alexis Descatha4 and 1.33 (95% CI: 0.9, 2.36). 1 Versailles Saint-Quentin-en-Yvelines University, UMRS 1018, Centre for Research in Epidemiology and Population Health, Population-Based Epidemiological Cohorts ” Research Platform, Villejuif, France, 2LUNAM P3.9 University, Laboratory of Ergonomics and Epidemiology in Occupational Direct and indirect effects in the presence of time-dependent confounding Health, University of Angers, Angers, France, 3Department of Occupational Georgia Vourli, Giota Touloumi Health, French Institute for Public Health Surveillance (InVS), Saint Maurice, France, 4AP-HP, Poincaré University Hospital, Occupational Health Unit, Athens University Medical School, Dpt. Of Hygiene, Epidemiology & Medical Statistics, Athens, Greece, Athens, Greece Garches, France P3.7 Standardization (STD), inverse probability weighting (IPW) and doubly robust estimation (DR) are recommended to estimate the Average Treatment Effect (ATE). Their asymptotic properties and theoretical variances are already well known. However, the properties of such ATE estimators in small samples have not been fully explored and few applications are available. Our aim was to compare the performance of ATE estimation methods in a specific example using simulations, in order to estimate the effect of a balanced exposure on a rare binary outcome using theoretical variances. In the present work, we consider a cross-sectional study conducted among 2161 men and 1549 women, showed 15 and 14 cases respectively, and a Causal methodology is mainly focused on the estimation of the total exposure’s effect. Our aim is to develop a method to generate data appropriate to be analyzed with Cox marginal structural models (msm), prespecifying the direct (immediate) and indirect (mediated via other variable(s)) effects and explore how the total effect decomposes into these components. Data were simulated assuming a time-dependent confounder, binary or continuous. Exposure’s direct and indirect hazard ratios were prespecified. Scenarios where the proportional hazards (PH) assumption is either met or violated were explored. Deviation of the total effect from the sum of the direct and indirect effects was assessed. For each scenario, 1000 datasets with 1000 individuals and 10-year follow-up were generated. ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info Assuming a binary marker, and under PH, the relative bias was -1.6%, with 95% empirical coverage probability 94.7%. When the PH assumption was violated, the total effect deviated from direct plus indirect effects by 3.3%111.5%, depending on the marker’s trends both on and off therapy, as expected. In such cases, we explored ways to adequately model the time dependent indirect treatment effect and the relation of the estimated total effect with the prespecified parameters. The proposed simulation method is approximate, as we prespecify direct and indirect effects instead of the marginal one. However, it is shown that if the PH assumption holds, the total effect can be obtained by adding the direct and indirect ones. The proposed simulation method could be useful when direct and indirect effects are available, even in non-PH cases. P3.10 85/156 To assess whether the effects of confounding have been reduced, one can examine the distribution of measured baseline covariates between treated and untreated subjects in the matched sample, or in the sample weighted by the IPTW, using the standardized differences (SDs). Then, the sample where the SDs are minimized should be selected. Because discrepancies according to the covariates may exist, one would need to choose based on a summarized value of the SDs, such as the sum or the mean of the SDs. Better, such a summarized method could take into account the relative importance, in terms of selection bias, of each one of the covariate X. The objective of this study is to develop and assess a summary measure (Z) of the SDs, that would enable to choose between different samples, and that would take into account, for each covariate X1,...,n, the strength of its association with the outcome. We will present the results of a series of Monte Carlo simulations in which we will study the ability of different Z for selecting the sample associated with the less biased estimation. Unfortunately, this poster has been withdrawn. P3.11 Investigation of pleiotropy in Mendelian randomisation studies that use aggregate genetic data Fabiola Del Greco M.1, Elinor Jones2, Victoria Jackson2, Irene Pichler1, Andrew Hicks1, Peter P. Pramstaller1, Nuala Sheehan2, John R. Thompson2, Cosetta Minelli1 1 EURAC research - Center for Biomedicine, Bolzano, Italy, 2Department of Heath Science, Centre for Biostatistics and Genetics Epidemiology, University of Leicester, Leicester, UK Genes can be used as instruments to provide estimates of the association between modifiable intermediate phenotypes and disease risk ("Mendelian randomisation", MR). Unlike direct estimates from observational studies, MR estimates are free of confounding or reverse causation, provided that some assumptions are met. The main assumption is the absence of pleiotropy, that is the gene influences disease only through the given phenotype. Assessing pleiotropy may be difficult even for well-studied genes, and the use of multiple genes can indirectly address the issue: if all genes are valid instruments, their MR estimates should vary only by chance. This can be tested using the over identification test, but the test requires fitting all genes in the same model and cannot be used when only aggregate results of gene-phenotype and genedisease associations are available. We present a simple approach based on the use of meta-analysis that combines MR estimates from multiple genes, where pleiotropy is assessed through investigation of presence and magnitude of between-instrument heterogeneity, using heterogeneity test and I2 statistics. We illustrate the approach with an example, a Mendelian randomisation study of the effect of iron blood levels on Parkinson´s disease that uses four genes. Through simulations mimicking our example, we investigate the performance of the approach under different scenarios, where presence and magnitude of pleiotropy in one or more genes are varied. P3.12 P4 Clinical trials P4.1 Application of the Parallel Line Assay to Assessment of Biosimilar Drug Products Jen-pei Liu1,2 1 National Taiwan University, Taipei, Taiwan, 2National Health Research Institute, Zhunan, Taiwan Biological drug products are therapeutic moiety manufactured by a living system or organisms. These are important life-saving drug products for the patients with unmet medical needs. Due to expensive cost, only few patients are accessible to life-saving biological products. Most of early biological products will lose their patent in the next few years. This provides the opportunity for the generic versions of the biological products, referred to as biosimilar drug products. The US Biologic Price Competition and Innovation (BPCI) Act passed in 2009 provides an abbreviated approval pathway for biological products shown to be biosimilar to, or interchangeable with, an FDAlicensed reference biological product. Hence, cost reduction and affordability of the biosimilar products to the average patients may become possible. However, the complexity and heterogeneity of the molecular structures, complicated manufacturing processes, different analytical methods, and possibility of severe immunogenecity reactions make evaluation of equivalence between the biosimilar products and their corresponding innovator product a great challenge for statisticians and regulatory agencies. We propose to apply the parallel assay to evaluate the extrapolation of the similarity in product characteristics such as pharmacokinetic responses to the similarity in efficacy endpoints with respect to continuous, binary, and censored endpoints. We also report the results of a large simulation study to evaluation the performance, in terms of size and power, of our proposed methods. Numerical examples are presented to illustrate the suggested procedures. P4.2 A new performance measure of propensity score model Statistical derivation of a responder definition for the reduction of hot flushes Emmanuel Caruana, Romain Pirracchio, Matthieu Resche-Rigon, Sylvie Christoph Gerlinger, Florian Hiemeyer, Thomas Schmelter Chevret Bayer Pharma AG, Berlin, Germany Hôpital Saint Louis, APHP, Paris, France The propensity score (PS) is increasingly used even in situations with small The clinical relevance of drug effects is often assessed considering the sample sizes or low prevalence of treatment. In such situations, one may need proportion of responders to treatment. Typically, the definition of who is a to select the variables to include in the PS model, to avoid model responder is given somewhat arbitrarily, e.g. as a 50% reduction of the overparametrization. There is a need to choose between different PS models, outcome measure from baseline. However, for patient reported outcomes including different covariates. Moreover, different approaches such as PS- empirically validated definitions of treatment responders can be derived using matching or Inverse Probability of Treatment weighting (IPTW) may be statistical methods. We performed a blinded data analysis from a placebo-controlled study to considered. 86/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info investigate the efficacy of a treatment for moderate to severe hot flushes in postmenopausal women. Patients recorded the number of moderate to severe hot flushes each day in a diary and assessed their satisfaction with treatment on a Clinical Global Impression scale. The primary outcome was the absolute changes in the weekly number of moderate to severe hot flushes. A non-parametric discriminant analysis with normal kernels and unequal bandwidths was performed to determine the cut-off values between the patients that felt minimally improved and those that felt unchanged or worsened. Similarly, the cut-off value between (very) much improved and minimally improved was calculated. Standard deviations for the cut-off values were assessed by bootstrapping. As a result, a responder was defined as having at least a (minimal) improvement of 19.1 hot flushes per week at week 4 and a (substantial) improvement of 40.3 hot flushes per week at week 12. This definition was agreed with the FDA in a type A meeting and subsequently used as an endpoint in a pivotal clinical trial. clinical trials. The contentious issues involve statistical operating characteristics, implementation, and clinical interpretation. Through simulation using bootstrapping technique on data from a real large clinical trial with two treatment arms, we assess the operating characteristics of three randomization approaches that are easily implementable -- simple (SR), stratified permuted block (SPB), and a recently proposed minimal sufficient balance algorithm (MSB), with the latter two adjusting for three covariates with strong prognostic values -- combined with two analysis approaches -- with and without adjustment of the three covariates. The results show that, as expected, the type I error probability for the adjusted analysis is slightly conservative for both SPB (0.049) and MSB (0.048), and more so if the analysis is not adjusted for the covariates (0.035 and 0.033, respectively). Power is similar among the three randomization methods for adjusted analyses, but the unadjusted analyses yield substantially reduced power. For other randomization performance metrics, SR and MSB are ideal in the probabilities of deterministic assignments (0) and correct guesses (0.5 and 0.54, respectively), but the MSB best controls the probability of significant imbalances in the covariates between the treatment arms. We conclude that SR with adjusted analysis may be the best approach, with a caveat that it may be more vulnerable to challenges in P4.3 trial result interpretation due to covariate imbalances. Simulation results under Optimisation of the two-stage randomised trial design when some participants additional scenarios also will be presented. have no preferred treatment. Stephen D Walter1, Robin M Turner2 P4.5 1 McMaster University, Hamilton, Ontario, Canada, 2University of Sydney, Is a Controlled Randomised Trial the Non-plus-ultra Design? An Advocacy for Sydney, New South Wales, Australia Comparative, Controlled, Non-randomised Trials In a two-stage randomised trial, participants are randomly divided into two Wilhelm Gaus, Rainer Muche subgroups. In one subgroup, treatments are randomly assigned, while in the other participants choose their treatment. One can then estimate the treatment University of Ulm, Ulm, Germany, Germany effect, and the potentially important effects of patients' preferences between Background. Many people consider a controlled randomised trial (CRT treatments (selection effects) and interactions between preferences and identical to a randomised controlled trial RCT) to be the non-plus-ultra design. treatment received (preference effects). However, CRTs also have disadvantages. The problem is not randomisation We previously determined the optimum proportion of participants to randomise itself. Today, patients are educated, self-determined, and self-responsible. The to the choice subgroup, when all participants have a preference (Walter et al., problem is to obtain informed consent for randomisation and masking of Stat Med, in press). Here we generalise this, so that some participants have therapies from today's patients according to current legal and ethical no treatment preference and so are re-randomised to treatment. The optimum standards. We do not want to de-rate CRTs, but we would like to contribute to allocation to the choice group now also depends on the proportion of the discussion on clinical research methodology. undecided participants. Situation. Informed consent to a CRT and masking of therapies plainly selects The optimum proportion in the choice group ranges between 40% and 50% for patients. The excellent internal validity of CRTs can be counterbalanced by most reasonable scenarios. It is lower if preferences for one treatment poor external validity, because internal and external validity are antagonists. In dominate, or if the proportion of undecideds is low; otherwise the optimum is a CRT, patients may feel like guinea pigs, this can decrease compliance, cause typically slightly below 50%. However, the variances of the selection and protocol violations, reduce self-healing properties, suppress unspecific preference effects increase if preference for one treatment dominates, or if therapeutic effects and possibly even modify specific efficacy. many participants are undecided. Discussion. A control group (comparative study) is most important for the These ideas will be illustrated using data from a 2-stage randomised trial degree of evidence achieved by a trial. Study control by detailed protocol and comparing medical vs. surgical management strategies for women with heavy good clinical practice (controlled study) is second in importance and menstrual bleeding, where approximately 70% of participants in the choice randomisation and masking is third (thus the sequence CRT). Controlled nonrandomised trials (CnRTs) are just as ambitious and detailed as CRTs. Four group had no treatment preference. We conclude that two-stage design can be optimised even when some examples are given. participants have no treatment preference. However the absolute variation in Recommendation. We recommend clinicians and biometricians to perform these estimates becomes large if the proportion of undecided participants is more high quality CnRTs. They combine good internal and external validity, better suit daily medical practice, show better patient compliance and fewer large. protocol violations, deliver estimators unbiased by alienated patients, and provide a clearer explanation of the achieved success. P4.4 Revisiting baseline covariate adjustment in randomization and analysis of P4.6 large clinical trials A biomarker-based designs for a controlled phase II trial in oncology Yuko Palesch, Wenle Zhao, Yanqiu Weng Natalja Strelkowa, Martin Stefanic, Thomas Bogenrieder, Frank Fleischer Medical University of South Carolina, Charleston, SC, USA Boehringer-Ingelheim Pharma GmbH & Co KG, Biberach, Germany Baseline covariate adjustments in randomization and/or in analysis have yielded a vast literature of pros and cons of adopting these approaches in We developed a statistical analysis framework for threshold determination of a ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info potentially predictive biomarker based on the outcomes of early phase clinical trials. For this approach we assume the knowledge of the distributional behavior for biomarker levels within the patient population originating from previous trials or in vitro data. We simulate biomarker-stratified patient recruitment and the conduct of an exploratory controlled phase II clinical study using progression-free survival as the primary endpoint. In our simulations the treatment success, measured on a hazard ratio scale, is assumed to be dependent on the biomarker level. Dependence is modelled by either a change point model or a piece-wise linear dependency. The results of our simulations provide estimates for a reasonable sample size and duration of the study as well as the precision of estimated cut off values for the biomarker level at which the investigated treatment becomes more effective than the standard therapy. These results can afterwards be used in the planning of e.g. a phase III enrichment design. P4.7 Regression model to analyze the continuous primary end point in RCTs when the treatment effect depends on baseline values of the outcome variable Mathai A.K.1, Murthy B.N.2 1 MakroCare Clinical Research, Hyderabad, Andhra Pradesh, India, 2National Institute of Epidemiology, Chennai, Tamil Nadu, India The primary aim of conventional randomized controlled clinical trials is to investigate the efficacy of test drug in comparison with standard drug / placebo for a particular disease. However, even after randomization, sometimes, the patients differ substantially with respect to the baseline value of the outcome variable and there could be a possibility that the response to treatment depends on the baseline values of the outcome variable. When there are baseline-dependent treatment effects, differences among treatments vary as a function of baseline level. Usually, in this situation, Analysis of Covariance (ANCOVA) is the analytical option for statisticians by considering the baseline value of the outcome variable as the covariate; treatment groups would be the factor and the final value of the outcome variable as the dependent variable. Although variation in outcome variable associated with baseline value is accounted for in ANCOVA, analysis of individual differences in treatment effect is precluded by the homogeneity of regression assumption. This assumption requires that expected differences in outcome among treatments are constant across all baseline levels. To overcome this difficulty, we propose a general approach for ‘n' number of treatment groups under two situations namely when the data follows a normal distribution or otherwise which will be demonstrated with real life data from clinical trials. P4.8 Confidence intervals for the ratio of AUCs in cross-over bioequivalence trials Thomas Jaki1, Martin Wolfsegger2 1 Lancaster University, Lancaster, UK, 2Baxter Innovations GmbH, Vienna, Austria 87/156 example. P4.9 Interaction of treatment with a continuous variable: simulation study of significance level for several methods of analysis Willi Sauerbrei1, Patrick Royston2 1 Institute of Medical Biometry and Informatics, University Medical Center Freiburg, Freiburg, Germany, 2MRC Clinical Trials Unit and University College London, London, UK Standard methods for modelling treatment-covariate interactions with continuous covariates are categorisation or an assumption of linearity. Both approaches are easily criticized, but for different reasons (1). To retain all information in the data and model the interaction flexibly, we have proposed MFPI, a parametric approach based on fractional polynomials (FPs). Essentially, MFPI extends the linear interaction model by allowing non-linear functions from the FP class. Four variants were suggested, providing greater or lesser flexibility. In general, the approach appears promising but further evaluation is still needed. We conducted a large simulation study featuring different scenarios with varying true functions and ‘well behaved' and ‘badly behaved' covariate distributions. We investigated type 1 error rates for the four MFPI variants. We also considered an approach in which FPs are replaced with regression splines including differing numbers of knots, a linear function, and models based on categorisation. Simulations were conducted in a univariate setting, but extensions to a multivariable setting are straightforward. The estimated type 1 error rates are close to nominal for most of the procedures, but for badly behaved data, increased error rates are seen in several scenarios. The importance of checking for interactions between treatment and a continuous covariate is illustrated by reanalysis of data from an RCT comparing two treatments in cancer patients. 1. Royston, P., Sauerbrei, W. (2004): A new approach to modelling interactions between treatment and continuous covariates in clinical trials by using fractional polynomials. Statistics in Medicine, 23:2509-2525. P4.10 Comparison of groups in the presence of bimodality John-Philip Lawo1, Tina Müller2 1 CSL Behring, Marburg, Germany, 2Bayer Pharma, Berlin, Germany Recently, there is an increasing number of published papers in various research areas [e.g. cancer, eye diseases, pain, RNA/gene expression or animal studies] that is based on bimodally distributed data. If the bimodality is ignored during data analysis and standard statistical tools such as t-test and Wilcoxon test (both based on unimodality of the data distribution) are employed, this incorrect choice of methods can result in misleading outcomes and a loss of power. As a remedy, we propose two strategies for a more appropriate way of dealing with bimodal data: The first one is based on an adaption of the KolmogoroffSmirnov-test. The second uses clustering to create subsamples for comparison. The two approaches are compared with the t-test and Wilcoxon test with regard to power by means of simulation studies, assuming small to moderate sample sizes. Results demonstrate better properties of the two proposed approaches in the presence of at least one bimodally distributed group. Cross-over designs are recommended for evaluation of average bioequivalence by regulatory agencies using the non-compartmental approach for estimation of pharmacokinetic parameters. Bioequivalence is typically assessed based on the confidence interval inclusion approach for the ratio of product averages and with the traditional bounds of 0.8 to 1.25. In this talk we will introduce a non-compartmental approach for obtaining confidence intervals for the ratio of AUCs in two sequence, two period crossover studies. The estimator and corresponding confidence interval are constructed to allow sparse sampling making the approach applicable in paediatric studies and when missing data are present. We evaluate the P4.11 performance of the intervals in a simulation study and illustrate it in an Analysis of multicentre trials with continuous or binary outcomes 88/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info Brennan C Kahan, Tim P Morris MRC Clinical Trials Unit, London, UK Many multicentre trials randomise patients using permuted blocks stratified by centre. It has previously been shown that stratification variables used in the randomisation process should be adjusted for in the analysis in order to obtain correct inference. Centre-effects can be accounted for either using fixed-effects or random effects for continuous outcomes, and either fixed-effects, randomeffects, generalised estimating equations, or Mantel-Haenszel procedures. These analysis methods are compared a large simulation study. For our simulations, we varied the numbers of centres, the number of patients per centre, the intraclass correlation coefficient, the method of randomisation, and the distribution of patients across centres. Results were compared in terms of type I error rate, power, and precision of treatment effect estimates. P4.12 Are Appropriate Outcome Measures Being Used in Open-label Randomised Trials? Suzie Cro, Sunita Rehal, Brennan Kahan Medical Research Council Clinical Trials Unit, London, UK Randomised controlled trials (RCTs) are the gold standard for estimating treatment efficacy. Blinding is an important design feature known to limit bias in RCTs, however in some situations blinding is difficult or impossible to achieve and consequently an open-label trial is necessary. Subjective outcomes could lead to bias if those in charge of assessing the outcomes are not blinded, as they may rate the outcome differently depending on treatment arm. Therefore, in open-label trials it is necessary to ensure that outcomes can be either objectively measured, or that those in charge of assessing the outcomes are blinded. We define different types of outcome measures, and discuss whether they are appropriate for use in open-label trials. We then review RCTs published in 2010 from four general medical journals (BMJ, The Lancet, JAMA, and NEJM) to investigate whether trialists are using appropriate outcome measures in open-label studies, and whether inappropriate outcome measures could lead to biased estimates of treatment effect. P4.14 Within-center imbalance after balanced allocation using minimization method: Center as a stratification factor in multi-center clinical trials? Shu-Fang Hsu Schmitz1,3, Hong Sun2, Qiyu Li3, Stefan Fankhauser3 1 Institute of Mathematical Statistics and Actuarial Science, University of Bern, Bern, Switzerland, 2Institut für Medizinische Biometrie und Medizinische Informatik, Universitätsklinikums Freiburg, Freiburg, Germany, 3Swiss Group for Clinical Cancer Research (SAKK), Coordinating Center, Bern, Switzerland Background: If the number of strata within a factor is large or/and the distribution among strata is uneven, treatment allocation might be unbalanced in some strata, even using minimization. Such imbalance is likely when center is a stratification factor in multi-center trials. It is recommended to perform analyses adjusted for stratification factors after balanced allocation. Objective: To compare within-center allocation imbalance between including and excluding center as a stratification factor. Method: Seven randomized phase II-III trials conducted by our group were identified. Hypothetical treatment allocation was generated using minimization for the given patient data and for simulated data from 54 scenarios, with or without center as a stratification factor. Within-center imbalance was compared. Results: Sample sizes of the seven trials are 33-319, each with 9-35 centers, and results of including/excluding center are Number combinations: 36-1120/4-32 Patients per combination: 0.1-1.9/2.1-39.9 Centers imbalance ≥2 patients (%): 0-31/10-59 Centers one treatment (%): 16-67/28-67 The proportion of centers with imbalance by ≥2 patients is higher when excluding center for minimization. A similar pattern is observed for the proportion of centers receiving one treatment, but the difference is small. The simulations confirmed the patterns and the difference in imbalance by ≥2 patients decreases when number patients per combination decreases. Conclusions: The advantage of including center in minimization for preventing ≥2 patients imbalance is weak for small trials with many combinations. The advantage for proportion of centers with only one treatment is generally small. Caution should be given to one-treatment centers due to difficulty for adjusted analyses afterwards. P4.13 Sample size corrections for varying cluster sizes when testing treatment effects in two-armed randomized trials with heterogeneous clustering P4.15 Math Candel, Gerard Van Breukelen Department of Methodology and Statistics, Maastricht University, Maastricht, A Semiparametric Accelerated Failure Time Mixture Model for Latent Subgroup Analysis of a Randomized Clinical Trial The Netherlands 1 2 When comparing two different kinds of group therapy or two individual Lily Altstein , Gang Li 1 treatments where patients within each arm are nested within care providers, Novartis Institutes for Biomedical Research, Inc., Boston, MA, USA, clustering of observations may occur in each of the arms. For such designs the 2University of California at Los Angeles, Los Angeles, CA, USA efficiency loss due to varying cluster sizes is studied, where other studies are We study a semiparametric accelerated failure time mixture model for extended in that allowance is made for between-arm heterogeneity in terms of estimation of a biological treatment effect on a latent subgroup of interest with (a) the intraclass correlation, (b) the outcome variance, (c) the average cluster a time-to-event outcome in randomized clinical trials. Latency is induced size and (d) the number of clusters. In case of a linear mixed model analysis, because membership is observable in one arm of the trial and unidentified in employing maximum likelihood estimation of the treatment effect, the the other. This method is useful in randomized clinical trials with all-or-none asymptotic relative efficiency of unequal versus equal cluster sizes is derived. noncompliance when patients in the control arm have no access to active The asymptotic relative efficiency is a weighted harmonic mean of the treatment and in, for example, oncology trials when a biopsy used to identify efficiency losses of two trials resulting from duplicating each of two treatment the latent subgroup is performed only on subjects randomized to active arms. From this expression Taylor approximations of the relative efficiency are treatment. We derive a computational method to estimate model parameters by derived. An extensive Monte Carlo simulation for small sample sizes shows iterating between an expectation step and a weighted Buckley-James when both approaches are accurate and allows for deriving general guidelines, optimization step. The bootstrap method is used for variance estimation. The based on both approaches, to compensate for the efficiency loss due to performance of our method is corroborated in simulation. We illustrate our varying cluster sizes when planning the sample size of a two-armed trial with method through an analysis of a multicenter selective lymphadenectomy trial heterogeneous clustering. for melanoma. ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info P4.16 Analysis of recurrent event data - an applied comparison of methods using clinical data Katrin Roth1, Vivian Lanius1, Peter Reimnitz2 1 Bayer Pharma AG, Berlin, Germany, 2Bayer Pharma AG, Wuppertal, Germany Clinical data is often observed as recurrent events, e.g. time to / number of exacerbations of chronic diseases in a given time period. In general, the aim is to reduce the number of unfavourable events through a certain intervention. There are several methods available for analyzing such data, either focussing on the time to (recurrent) event(s) or the number of events. We compared the results of analysing real clinical recurrent event data by a Cox proportional hazards model for time to first event, an Andersen-Gill model for time to recurrent event, and generalized linear models using a cumulative logit distribution, Poisson distribution and negative binomial distribution for modelling the number of events. While all methods gave similar results in terms of parameter estimates and pvalues, a closer looked showed differences between the methods. Considering the time to event, there was no gain in information in estimating the treatment effect when analyzing recurrent events as compared to the simple time to first event analysis. Regarding the different generalized linear models, the Poisson model needs to be adjusted for overdispersion to provide at least a reasonable fit. The cumulative logit model intuitively seems less appropriate for count data, but provides a reasonable fit as long as higher number of events are combined as one category. The best fit was observed for the model based on the negative binomial distribution, which was also observed for other clinical count data. P4.17 Probability of Inferiority in Current Non-Inferiority Trials Primrose Beryl Gladstone, Werner Vach Institute of Medical Biometry and Medical Informatics, Freiburg, Germany 89/156 P4.18 Blinded estimation of within subject variance Rachid el Galta Merck Research Laboratories, MSD, Oss, The Netherlands For a randomized, double-blind, placebo-controlled, 4-period cross-over noninferiority trial, an initial sample size was estimated using an expected treatment effect and a presumed within subject variance of the normally distributed primary endpoint. Since there was uncertainty regarding the within subject variability a blinded interim analysis was planned to estimate the within subject variance and thus re-estimate the sample size. Different methods for blinded estimation of within group variance in randomized clinical trials are available. Gould and Shih (1992) have proposed an EM algorithm based procedure to account for missing information on treatment status. Chen and Kianifard (2003) and van der Meulen (2005) considered using a randomization block of size 2 or 4 and used the maximum likelihood estimate of the mixture of normally distributed data. In contrast to a parallel design, power calculations for cross-over trials are usually based on within subject variance. This presentation shows how these existing methods were applied to estimate the within subject variance of the primary endpoint of the trial above at the blinded interim look. References Gould AL, Shih WJ. Sample size re-estimation without unblinding for normally distributed outcomes with unknown variance. Communications in Statistics (A) -Theory and Methods 1992; 21:2833-2853. Chen M, Kianifard F. Estimating treatment difference and standard deviation with blinded data in clinical trials. Biometrical Journal 2003; 45:135-142. van der Meulen E. Are we really that blind? Journal of Biopharmaceutical Statistics 2005; 15:479-485. P4.19 Analysis of case scenario cross-over trial: an application of medical devices Background: The risk of accepting too many inferior new treatments has been posed as an manikin study inherent disadvantage of non-inferiority trials. One of the major determinants of Naohiro Yonemoto, Haruyuki Yuasa, Hiroyuki Yokoyama, Hiroshi Nonogi this risk is the non-inferiority margin. There has been some evidence of wide National cente of neurology and psychiatry, Tokyo, Japan variability of the non-inferiority margin in practice. The aim of our study is to quantify the risk of accepting inferior treatments among current non-inferiority Background: Analysis of a cross-over trial with sequence scenario trials and to assess the impact of the non-inferiority margin on the risk. (intervention) in medical devices training using manikin motivated our work. We illustrated how inferences a scenario directly and indirectly effect for outcomes. Methods: We collected two datasets of current non-inferiority trials - registered and Methods: We consider strategies for modeling repeated sequential scenario published trials. For each NI trial, we calculated its probability of inferiority and and focus some model-based approaches. The issues are addressed using the the true treatment effect based on the margin used and the used sample size example of comparison with two medical devices for the first intubation attempt for three empirical pre trial distributions - optimistic, moderate and pessimistic using a manikin setting. The outcome is binary data (success or failure). We try scenarios where the average true treatment effect is 0, slightly negative and to perform some models (mixed-effect, GEE and MSMs) and compare distinctly negative respectively and rate of superiority of the new treatments and check some assumptions. being 50%, 16% and 2.5% respectively. Distributions of the inferiority Results and Conclusion: The model based approach appears to be more probabilities and the treatment effects among these current NI trials were suitable for analysis the study design. We need to select appropriate model for looked at to reflect the current risk of having accepted inferior treatments in the study design and results of interpretation. successful NI trials. Results: P4.20 Standardised margins were obtained from the extracted data belonging to 49 registered NI trials and 104 published NI trials. Corresponding inferiority Investigating the Strength of the Association between the Amplitude of the probabilities suggested a substantial amount of inferiority among the new Impedance Cardiogram (ICG), Thrust and Depth during CPR compressions treatments which were accepted in these trials, even with the assumption of an Paul McCanny1, Gloria Crispino-O'Connell1, Rebecca DiMaio1, Andrew Howe2, optimistic pre-trial probability. Under the moderate scenario, we could observe David McEneaney3, Paul Crawford1, David Brody1, John Anderson1 a distinct trend towards degradation of treatment effects in successful trials. 1 Heartsine Technologies, Belfast, N. Ireland, UK, 2Queen's University Belfast, Belfast, N.Ireland, UK, 3Craigavon Area Hospital, Belfast, N. Ireland, UK Background: Distinctive changes in the morphology of the Impedance 90/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info Cardiogram (ICG) related to chest compressions can be used to assess Cardiopulmonary Resuscitation (CPR) efficacy. Following retrospective analysis of the ICG traces, it was observed that the changes in impedance cardiogram during compressions are of higher amplitude when compared to the changes in normal perfusing rhythms. There was evidence that the amplitude of the ICG varied according to the force applied to compressions. Design: The objective of the study is to establish the strength of the association between thrust (force) and ICG amplitude during compressions and between depth and ICG amplitude in a porcine model and a human pilot study. The primary variable is the relationship between thrust and ICG amplitude, in terms of correlation coefficients. The method used for the computation of the primary variable is the Bland-Altman approach which computes correlation coefficients with repeated measures [1], where the subject is modelled as a fixed effect term. Results: This study found some evidence that the relationship between thrust and ICG (in terms of ADU) could be modeled using a quadratic expression. The relationship between Depth and ICG was also investigated and it was found that the depth was highly correlated with the ICG (in terms of ADU). Those results suggest that ICG has the potential to be used as a measure of the quality of compression during CPR. effects, as for judging the discrimination ability of different statistical models. The logistic model as well as the generalized linear mixed model (GLMM), are widely used for predicting probabilities of the positivity of a disease or condition, and the estimated probability is then used as a biological marker for constructing the ROC curve and computing the area under the curve. The propensity score is widely used for the estimation of average predicted treatment effects in non randomized studies. Furthermore, calculated summary statistics of the area under a ROC curve have already been proposed in the context of repeated measures design. We compare two approaches of computing the prediction through the area under the ROC curve in the case of a therapeutic decision in critical condition. An example deals of the need of sedation in ICU unit. The aim is to provide comfort and minimize anxiety. However, adverse effects of a deep sedation are noteworthy, and the optimal end point of sedation in intensive care unit patients is still debated. We analyzed if a level 2 on the Ramsay Scale (ie, awake, cooperative, oriented, tranquil patient) is suitable for an invasive therapeutic approach. Along with the need to optimize the use of ICU resources, "conscious" sedation is becoming increasingly attractive in the ICU. Guidelines suggest that sedation should be individualized and administered into shortest time at the lowest effective dose. P4.23 P4.21 Optimization of managing the lost to follow up patients in a Phase II oncology New approaches for design and analysis of pediatric pharmacokinetic and trials pharmacokinetic/pharmacodynamic studies Lisa Belin1, Philippe Broët2, Yann De Rycke1 1 Jixian Wang, Kai Grosch, Emmanuel Bouillaud Institut Curie, Paris, France, 2JE2492, Paris Sud University, Villejuif, France Novartis Pharma AG, Basel, Switzerland Phase II oncology trials currently evaluate a binary endpoint, usually the Ethical and practical constraints in pediatric clinical pharmacology studies response to treatment. This endpoint is evaluated at a given point in time, restrict pharmacokinetic (PK) sampling in young children. Frequently, only which is the same for all subjects. But a problem occurs when some patients sparse blood sampling is feasible, preventing the application of non-parametric are lost to follow-up. Classical study designs such as Fleming or Simon’s plans calculation of individual PK parameters, hence population PK modeling is the are not adapted to this situation. The decision rule is based on a binary common PK analysis and the basis of PK-pharmacodynamic (PD) criterion. On which patients should the response rate be evaluated? analysis. The application of population PK models for children data requires a The following approaches are usually used: structural model which may not been established or is only applicable to adults. - Consider patients lost to follow-up as treatment failures Applying inadequate popPK models may lead to inadequate model fitting, uncertainty in model selection and parameter estimation. As a model - Exclude patients lost to follow-up. independent approach we propose a non-parametric estimation for PK - Estimate the response rate by the Kaplan-Meier method by censoring parameters and time profile based on linear mixed models and interpolation patients lost to follow-up at the date of their last known contact. The stopping using properly designed sparse sampling scheme. To determine PK sampling boundary defined in the protocol need to be converted on percentage. schemes we propose using D-optimal designs for nonlinear mixed population - Replace patients lost to follow-up by new included patients. PK models as the basis, but also consider their performance in non-parametric If the number of patients analysed is different from the number of patients PK parameter and time profile estimation as well as robustness to parameter planned to be included, the stopping boundary will also be converted on misspecification. We also consider optimal designs for non-parametric percentage. estimation based on mixed model approach and practical implementation of multi-objective designs for model non-model based approaches, modeling of Our objective is to study how censoring can interfere with classical phase II PKPD relationship as well as model validation. To confirm small sample analysis plans in terms of bias, type I and type II errors and the increase of properties of these designs, we propose using simulation for fitting population sample size required for the analysis in order to make recommendations on the PK models and non-parametric PK parameter estimation. The proposed use of the strategies mentioned above. This study will be carried out on approaches were applied to design of a pediatric trial under real scenario and simulated data. Simulations are done according to Simon’s design parameters and rate of lost to follow-up patients. The strategies were also applied to real practical issues and considerations were examined. data. P4.22 P4.24 Propensity score and area under a ROC curves in repeated measures clinical Blocking in Unblinded Randomized Clinical Trials studies Robert Parker Alberto Morabito University of Michigan, Ann Arbor, MI, USA Università degli Studi di Milano, Milano, Italy, Italy The receiver operating characteristic (ROC) curve is widely used for Statistical Blocking is routinely used in randomized clinical trials. It ensures treatment Evaluation of Medical Tests for Classification and Prediction of treatment groups are balanced for temporal and seasonal factors. In multi-center studies, randomization is frequently stratified by site to ensure that site specific factors ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info are balanced across treatment groups. When a study is double blind, blocking would appear to have substantial potential advantages without minimal disadvantages. In some studies, however, the treatment received is known by study staff. This is especially common in clinical effectiveness trials which often forgo any attempt to mask treatment received. As an example, in a study assessing the use of 6 months of continuous positive airway pressure (CPAP) in children with sleep problems, no sham treatment would be used in the control (standard of care; SOC) group. If blocking is used, study staff will be able to predict to a greater or lesser extent whether the next potential participant will be allocated to CPAP or SOC. This can have substantial impact on how actively study staff attempt to enroll a potential participant. Because CPAP requires considerable effort to be administered successfully, if staff expect the next participant to be allocated to CPAP, staff may (consciously or unconsciously) preferentially enroll "compliant" "dedicated" and "committed" parents while attempting to avoid enrolling parents expected to be less diligent in administering CPAP. In this presentation we assess the ability of staff to correctly guess the treatment assignment with common blocking schemes. More imprtantly, we also suggest variations from perfect blocking which will reduce such problems. P4.25 Non-Inferiority Trials of Non-Pharmaceutical Interventions Karen Smith University of Leicester, Leicester, UK Both the FDA and EMEA have produced guidelines for pre-authorisation (preapproval) non-inferiority trials of pharmaceuticals. Ideally such trials have two aims; to establish, indirectly, the effect of a new intervention relative to placebo and to estimate the relative effect of the new drug compared to a reference drug. There has been a recent increase in the number of non-inferiority trials conducted to evaluate non-pharmaceutical interventions and it's not clear whether, and in what ways, the regulatory guidelines might be useful in the design of such trials. Particular issues of concern include the circumstances in which a non-inferiority trial would be the most appropriate design, the choice of primary and secondary outcomes, choice between a single primary outcome or co-primary outcomes and determination of the non-inferiority margin. Reporting of non-inferiority trials is acknowledged to be poor and in 2006 an extension to the CONSORT statement was published. A review of noninferiority trials reported since 2007 has been conducted focussing on adherence to the reporting guidelines. The particular design issues highlighted by this review will be presented, with particular reference to nonpharmaceutical interventions. 91/156 how the readers interpret the SoR. Optimization with an internal SoR requires that reader interpretation of the test and comparator are correlated, in aggregate, with the interpretation of the SoR. R = (r1/σ2σ3)+(r2/σ1σ3)+(r3/σ1σ2) where σ-standard deviation r-correlation. This was used to select 3 readers from a pool of 9 readers for a Phase_III trial. The aggregate correlation values varied from -0.7 to 0.6, demonstrating there are better and worse combinations of readers for optimal outcome measures. This metric was not identical to the cluster of readers with highest intra-reader correlation because high inter-reader correlation may be consistently wrong when compared to the internal SoR artificially reducing sensitivity/specificity and increasing variance. Alternatively, selecting readers that correlate with the internal SoR will decrease reader effect. This is independent of the test versus comparator since they were pooled, eliminating bias. This minimizes reader impact so that only pure differences between efficacy of the compound versus the comparator remain. P4.27 Cost and Prevention Strategies of Randomization Errors in Emergency Treatment Clinical Trials Wenle Zhao, Valerie Durkalski, Jordan Elm, Catherine Dillon, Yuko Palesch Medical University of South Carolina, Charleston, SC, USA Errors occurring in the subject randomization process affect the results of clinical trial analysis. Trials treating emergency conditions, such as stroke and traumatic brain injury, are especially vulnerable to randomization errors due to the short time window between injury onset and randomization. A randomization error may result in a subject receiving a treatment other than the one assigned by the randomization algorithm. This presentation evaluates the impact of randomization errors on type I error, power and the potential biases for two-arm clinical trials with a binary outcome testing for superiority or noninferiority with intent-to-treat analysis and per-protocol analysis. For studies testing the superiority and analyzed under the intent-to-treat principal, two additional subjects are required to recover the power reduction caused by each cross-over. For non-inferiority studies, the impact of these errors could result in an increase to the type I error rate. Randomization errors may further reduce the credibility of the trial result as these errors may be associated with suspicion of selection bias. Case reviews of randomization errors in several large NIH-funded multicenter emergency treatment trials are presented. Many factors may contribute to randomization errors, including eligibility errors, study drug inventory management, user permission errors and technical difficulties for web-based central randomization systems, and human mistakes at various levels. Our lessons learned may help to protect the quality and efficiency of future clinical trials. P4.26 P4.28 Short interrupted time series designs in clinical practice and policy research: an analysis approach using restricted maximum likelihood Andrew Forbes1, Muhammad Akram1, Catherine Forbes1, Craig Ramsay2 1 2 A preponderance of clinical trials rely on imaging_endpoints for Monash University, Melbourne, Victoria, Australia, University of Aberdeen, primary_efficacy which require blinded_reader interpretation to eliminate any Aberdeen, Scotland, UK clinical bias. This blinded_reader selection has become increasingly critical to Interrupted time series designs are often utilised in medication use research, the success of trials so that reader effect is minimized. hospital infection control, health technology assessment, population health and Reader selection is often performed by review of resumes which is weak numerous other areas. The data consist of the repeated observation of a evidence. Readers are sometimes selected based on their inter-reader variable in a defined population before and after a population level consistency. We evaluated whether reader selection can be optimized for the intervention. These time series are often very short in length, and as such they pooled outcome variables, test and comparator when there is no pose challenges to the use of routine statistical methods for time series gold_standard, like pathology. Many trials have an internal, image based, analysis, largely due to their poor estimation of the required autocorrelation standard_of_reference(SoR) where optimal reader selection is dependent on parameters. How to Select Readers for Clinical Trials When There is No Gold Standard Jacob Agris1, Dan Haverstock1, Aida Aydemir1, Julie Agris2 1 Bayer HealthCare, Pine Brook, NJ, USA, 2Hofstra University, Hempstead, NY, USA 92/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info In this talk we consider a regression model with AR(1) errors for short interrupted time series’. Prior work by others with this model has not produced estimators with desirable properties apart from a proposed doublybootstrapped bias-and-variance-corrected estimator. In this talk we evaluate maximum likelihood (REML) estimation not previously applied in the interrupted time series literature. Using simulations we compare the performance of regression model parameter estimators using REML with that of a variety of existing estimators, both with and without double application of the bootstrap. We further evaluate a Satterthwaite degrees of freedom estimation approach both with and without an expected information matrix modification. Our results indicate that the performance (bias, size, power, CI coverage) of the REML estimator with corrected degrees of freedom matches or exceeds that of double-bootstrapped estimators. This finding has the potential to enable simpler and more efficient analyses of short interrupted time series’ as well as providing opportunity for a detailed study of design aspects of such series’ currently lacking in the literature. Method: Using a cohort of 469 RCTs published between 1 July and 31 December 2009 indexed on PubMed, we will identify RCTs with parallel arms reporting time-to-event data as the primary outcome (PO). Studies of crossover, cluster or factorial trials will be excluded. Data on whether studies reported a sample size calculation for the PO and, if so, how the sample size was derived will be extracted. In particular, we will record the nature of the PO (e.g. overall survival, time to death, time to relapse), the overall number of participants (and events), the method used to calculate the sample size, how the effect size for the sample size calculation was derived and the length of follow-up used in the calculation. We will also document the actual length of follow-up for the PO, the actual HR and whether the final analysis was adjusted or unadjusted. Results and Discussion: We will present a summary of the findings from this review of published trials at the time of the meeting. P5 Consulting P5.1 P4.29 A Bayesian approach to assess the active control treatment effect for the 10 tips for enhancing biostatistical consultations or collaborations in clinical research: lessons from the trenches design of non-inferiority trials Lehana Thabane Gang Li, Krishan Singh, Jeffrey Wetherington, Linda Mundy McMaster University, Hamilton, Ontario, Canada GlaxoSmithKline Pharmaceuticals, Collegeville, Pennsylvania, USA Interpretation of a non-inferiority (NI) trial requires compelling historical The Biostatistics and Methodological Innovation Working (BMIW) Group is one evidence of sensitivity to drug effect for the selected active control treatment. of several working groups within the CANadian Network and Centre for Trials The active control treatment effect is usually obtained from historical trials in INternationally (CANNeCTIN).In addition to advancing biostatistical and order to determine a clinically acceptable NI margin. In the absence of placebo- methodological research, and building biostatistical capacity in cardiovascular controlled historical trials in the target population, the effect size has diseases (CVD) and diabetes mellitus (DM), we aim to enhance CVD/DM traditionally been estimated indirectly by first calculating the difference between research through collaborating with clinician investigators on their studies. the lower bound of the confidence interval for the active control and the upper Providing effective statistical consultation or collaboration in clinical research bound of the confidence interval for placebo proxy, followed by additional often requires skills not often taught in most graduate statistical programmes. discounting. The discounting factor is subjective, which often has significant After numerous collaborations - with multiple errors and others that worked implications on the NI margin and sample size. We propose a Bayesian well, I share 10 tips based on the lessons I learnt through working and approach to derive the active control effect which eliminates the additional step interacting with clinician investigators on more than 100 research projects. of subjective discounting. In this approach the control and placebo responses These are by no means the only issues to worry about - I learned a lot more are considered as random variables for which the distributions are estimated through trial-and-error, but hopefully these provide a good starting point for from historical data. The active control effect is then determined by comparing things to consider in enhancing your own collaborations. these two probability distributions. This quantitative approach provides an objective assessment of the control effect size by using Bayesian probability to P6 Diagnostic methodology rule out extreme or improbable treatment effect. An appropriate NI margin can be determined from this estimate using clinical and statistical reasoning. This P6.1 proposed approach will be illustrated for efficacy endpoint in complicated Average kappa coefficient: a new measure of accuracy of a binary diagnostic test urinary tract infections and the mortality endpoint in nosocomial pneumonia. Jose Antonio Roldán Nofuentes, Juan de Dios Luna del Castillo Biostatistics, School of Medicine, University of Granada, Granada, Spain P4.30 Sample size calculation for time-to-event outcomes in randomized controlled trials: A review of published trials Ly-Mee Yu1, Mike Bradburn2, Milensu Shanyinde1, Sally Hopewell1, Gary Collins1, Merryn Voysey1, Omar Omar1, Rose Wharton1 1 University of Oxford, Oxford, UK, 2University of Sheffield, Sheffield, UK Background: Systematic reviews on sample size calculation are well documented. However, formulae for estimating the sample size for time-toevent outcomes are more complicated, since the sample size depends primarily on the number of events expected during the study. In addition, common methods are based on the proportional hazard assumptions, i.e. a constant hazard ratio (HR) over the study period. Objective: To critically evaluate the methods used to calculate sample sizes for time-to-event outcomes in a representative sample of randomized controlled trials (RCTs). A diagnostic test is a medical test that is applied to a patient in order to confirm or discard the presence of a particular disease. When the result of a diagnostic test is positive or negative, in which case the diagnostic test is called a binary diagnostic test, its discriminatory accuracy is measured in terms of sensitivity and specificity. Another parameter used to assess the performance of a binary diagnostic test is the weighted kappa coefficient, defined as a measure of the beyond-chance classificatory agreement between the diagnostic test and the gold standard. The weighted kappa coefficient of a binary diagnostic test depends on the sensitivity and specificity of the diagnostic test, on the disease prevalence and on the relative loss between the false positives and the false negatives (called the weighting index). The problem in the use of the weighted kappa coefficient as a measure of the effectiveness of a diagnostic test is the assignation of values to the weighting index. In this work, we propose a new measure of classificatory agreement between the diagnostic test and the gold standard: the average kappa coefficient. The average kappa coefficient depends on the sensitivity and the specificity of the diagnostic test and on the ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info 93/156 disease prevalence, but does not depend on the weighting index, and it is a In order to overcome these problems we have innovated refinements to measure of the beyond-chance average classificatory agreement between the nomograms by centring on a reference non-case. Furthermore, distributions of diagnostic test and the gold standard. the covariates are visually represented in the centred nomogram. We exemplify this by using simulated and Framingham data set. Special emphasis will be put on non-linear relationships and leverage points. P6.2 We conclude that our refinements can increase acceptance and practical use Applying Partial Least Squares Discriminant Analysis (PLS-DA) for optimisation of nomograms by the clinical community. of decision rules based on complex patient-reported data: creation of the FibroDetect® scoring method P6.4 Hélène Gilet, Benoit Arnould, Antoine Regnault Comparative assessement of a new imaging technique versus an imperfect Mapi Consultancy, Lyon, France invasive gold standard: early detection of coronary stenosis after arterial switch Background: Information from patients can be unique and essential to medical surgery in children decision. However, heterogeneity of this useful information may lead to Phalla Ou3, Kevin Yauy1, Kim- Hanh Le Quan Sang4, Gregory Nuel2, Jeancomplex data that are challenging to comprehend. Partial Least Squares (PLS) Christophe Thalabard5 analysis offers an attractive solution to deal with issues associated with such 1 2 data, as it is able to account for multicollinear variables, incomplete data, and Paris Descartes University, Paris, France, MAP5, UMR CNRS 8145, Paris, 3 4 large numbers of variables. PLS Discriminant Analysis (PLS-DA) was applied France, Pediatric Radiology, Necker Hospital, APHP, Paris, France, Inserm 5 U781, Paris, France, Diagnostic Center, HotelDieu, APHP, Paris, France, to data collected with the 80-item draft version of the FibroDetect 6 (questionnaire designed to help primary care physicians (PCPs) detect Sorbonne Paris Cite, Paris, France potential fibromyalgia patients) to create the optimal scoring rule for the tool. The arterial switch operation for transposition of the great arteries requires Methods: An observational, prospective, non-drug study was conducted to transfer of the coronary arteries from aorta to the proximal pulmonary artery validate the FibroDetect questionnaire as a screening tool in 276 patients with (neo-aorta). In 8-10% of cases, there is evidence of late coronary stenosis, undiagnosed chronic widespread pain. PLS-DA was applied to patients' often asymptomatic but with clinical consequences. Surviving children are responses to FibroDetect items, to select the most relevant items for generally proposed routine coronary angiography under general anesthesia as separation of potential fibromyalgia from non-fibromyalgia patients and create a it remains, though imperfect, the gold standard for early detection of stenosis. simple scoring method that allows for its quick use by PCPs. Resulting This invasive procedure is now challenged by non invasive high resolution discriminant models were evaluated using the Area Under the ROC Curve multislice CT (Ou P et al, JACC Cardiovasc Imaging. 2008) (AUC). In order to compare the performances of the new procedure to the reference Results: The first PLS-DA enabled a first set of 35 relevant items to be ones, data from three studies involving 379 children exposed to both identified. Further PLS-DA were performed after item coding simplification and procedures have been collected in two cardiac centers, using the same different scoring methods were tested (AUC from 0.71 to 0.75). The final model protocol. included 9 items (AUC=0.74), resulting in a 0-9 score with a cut-off of 6 for The coronary vessels were considered as a set of M = 6 correlated sites fibromyalgia suspicion. (m=1,..,M). For each child (j=1..ni) within each study (i=1..3) and for each Conclusion: PLS-DA is a powerful statistical method to contribute to the technique (k=1,2), each site was rated 1/0 according to the presence of creation of PRO screening tools in the context of complex data. stenosis. The observations Yijkm were modelled using a generalized mixed model logit(P(Yijkm=1/D,Test) = µ + a1.D + a2.Tk + b1.Zj+ b2.Zm where D is the unobserved true disease status, Tk the imaging technique, b1.Zj P6.3 and b2.Zm a subject's random effet and a site random effect, respectively, in Refined nomograms to enhance the interpretation of clinical risk prediction order to take into account different sources of correlation between models observations. Juan V. Torres-Martin1, Harald Heinzl2, Jordi Cortés3, Harbajan Chadha- Data were analysed using an EM algorithm taking advantage of the standard R Boreham1 function glmer {lme4} via adapted weights. The performances were studied 1 Biostatistics department, Actelion Pharmaceuticals Ltd., Basel, Switzerland, using simulated data sets of correlated binary variables (Qaqish, Biometrika, 2 Center for Medical Statistics, Informatics, and Intelligent Systems, Medical 2003). University of Vienna, Vienna, Austria, 3Department of statistics and operations research. Technical University of Catalonia, Barcelona, Spain A risk prediction score can straightforwardly be derived from a multivariable regression model. For ease of clinical use, the regression formula is often coarsened into a point score. Alternatively, it can be translated into a nomogram (e.g. with R software) avoiding loss of precision and improving interpretation by means of a graphical presentation. This enables visualization of the contribution of individual covariates to the overall prediction score. These features improve clinical acceptance of a risk prediction tool, as clinicians do not have to base their decisions on a black box. The risk score in conventional nomograms is left-aligned, starting from a subject with the smallest possible value of each covariate. In such a nomogram, highly specific covariates (primarily identifying non-cases) can contribute a lot of points for clinically normal findings, compared with sensitive covariates that primarily identify cases. This can cause problems with clinical interpretation, even though the final risk prediction based on the contribution of all the covariates is correct. P7 Epidemiological designs P7.1 Cancer incidence and prevalence: application of mortality data to estimates and projects for the period 2001-2015, Iran Mohammad Reza Maracy, Farhad Moradpour, Sayed Mohsen Hosseini Isfahan University of Medical Science, Iran, Iran Background: The aim of this study was to show up-to-date estimates of incidence and prevalence in Isfahan for all cancers except non melanoma skin cancer over the period 2001-2010 to provide projections up to 2015, based on a statistical method that uses of mortality and cancer patient survival data. Methods: Mortality data in Isfahan is collected from various sources such as hospitals, medical forensic, cemetery and health centers. In addition population data by sex, age, location and calendar year in the period of 2001-2010 were acquired from the Statistical Center of Iran. Relative survival probabilities for all 94/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info cancers combined and for selected specific cancers were estimated based on observed cancer death and expected mortality data. Incidence and prevalence estimates were computed with Mortality-incidence Analysis Model (MIAMOD) method. Results: The estimated age-standardized cancer incidence rate had higher increase rate for urban females than for males. Also the number of prevalent cancers was higher among females, which was mostly due to better cancer survival rates in women. Age adjusted incidence was estimated to increase by 6.9 and 8.7 per 100000 annually between 2001 and 2015 in males and females, respectively. The prevalence is to increase 24 and 40 and mortality 2.8 and 2.5 per 100000 between 2001 and 2015. Conclusion: The present study does not only have show the incidence and prevalence estimates of all cancers combined, but also gives information about cancer burden which can be used as a bases for planning healthcare management and allocating recourses in public health. Data was available from the 2010 and 2011 cohorts of children transferring from local authority or public-private partnership preschools to primary school at approximately five years of age. The main outcome was total strengths and difficulties questionnaire (SDQ) score. There was complete data on total SDQ, postcode and demographics for around 3000 children in each of the two years. A mixed-effects zero-inflated negative binomial model of SDQ score on age, gender, deprivation, and looked after status was fitted with year, nursery establishment attended and area as random effects. The results were superimposed onto a map of the city. A further map of smoothed SDQ score was produced from a model incorporating spatial correlation. The results show that there was considerable variability between areas in average SDQ score, with many having scores that were substantially better or worse than would be expected based on their average demographics. Areas in the least deprived quintile had on average better scores than those in the other four. Younger children, boys and those looked after in care tended to have more difficulties. As the PSF progresses, data will become available on all children in Glasgow P7.2 across many years. Evaluation of the areas containing children that continue Selection bias in obesity research: when do sampling weights solve the to struggle will enable more targeted interventions. problem? Ralph Rippe, Saskia le Cessie, Martin den Heijer, Frits Rosendaal P7.4 Leiden University Medical Center, Leiden, The Netherlands Pesticides exposure in an apples growing valley (Trentino - Italy): Selection bias is a well-known phenomenon in (epidemiological) research epidemiological study designs. Riccardo Pertile, Martina De Nisi, Silvano Piffer It can obscure relationships and causal pathways, while this effect is not trivially detected (Hernan et al, 2004). Index-event bias (Dahabreh & Kent, Department of Clinical and Evaluative Epidemiology - Centre for Health 2011) can be seen as a special case of selection bias, in which subjects are Services of Trento, Trento, Italy included based on an index-event. Previous epidemiological studies have indicated that pesticide exposure is The inclusion of reference observations from the general population can possibly associated with many diseases and health problems. However, provide more insight in the effect of selection bias. However, if the study considerable heterogeneity has been observed in results. This epidemiological population and reference observations are sampled with different probabilities, surveillance study aims to test possible associations between pesticide the need of accounting for sampling weights strongly depends on the type of exposure and health problems, such as tumours, non Hodgkin Lymphoma, model and type of selection (Lumley, 2010). For example, some confounder leukemia, congenital anomalies, miscarriages, stillborn babies, preterm and corrections implicitly correct for selection imbalance. low weight births, asthma, rhinitis and Parkinson disease, during the period Here, we elaborate on circumstances under which and in what way selection 2000-2009. The area covered by the study consists of 38 municipalities in a bias influences the relation between exposures and outcomes. This will be mountain valley (Valle di Non) in Trentino region (north east of Italy), very done in a simulation study and in data from the NEO (Netherlands famous for apples growing. Not the whole valley is dedicated to apples Epidemiology of Obesity) project. In the NEO study more than 5000 production, so that it has been divided in two areas, according to some criteria participants with a BMI above 27 kg/m2 are included, together with a reference such as the percentage of hectares cultivated with apples within each group of 800 subjects with a normal BMI. We consider situations as: municipality, or the altitude. The municipalities supposed to be at risk were 24, all placed in the low valley, with a more temperate climate. Using existing 1) BMI is a cause of the exposure informative flows and specific health registers, incidence and prevalence rate 2) BMI is a risk factor for the outcome ratios (with Fay-Feuer 95% confidence intervals) have been calculated for each 3) BMI is affected by the outcome of interest year comparing the two areas in the following health field: cancers (20002006), miscarriages, asthma, rhinitis and Parkinson disease (2000-2009). As 4) BMI is a causal intermediate regards congenital anomalies, stillborn babies, preterm and low weight births, a 5) and combinations of (1), (2), (3) and (4), logistic regression analysis has been carried out, using as covariate the and we discuss when the use of the reference group with appropriate sample permanent address of the mother (at risk or not at risk area) to check the weights is needed. possible association with the dependent variables (presence of congenital anomaly in newborns, stillbirth, etc.). P7.3 The Parenting Support Framework in Glasgow: mapping variability in P7.5 behavioural difficulties Use of linked registries in the design of cohort studies, a tool against selection Sarah Barry, Lucy Thompson, Louise Marryat, Jane White, Philip Wilson bias: the Constances example. University of Glasgow, Glasgow, UK Rémi Sitta, Alice Guéguen, Julie Gourmelen, Diane Cyr, Marie Zins, and the Parenting is the single major factor implicated in health outcomes for children. Constances team This ongoing study aims to establish the variability in behavioural difficulties Versailles Saint-Quentin-en-Yvelines University, UMRS 1018, Centre for across the city after the recent introduction of the whole population Parenting Research in Epidemiology and Population Health, `` Population-Based Epidemiological Cohorts” Research Platform, Villejuif, France Support Framework (PSF) in Glasgow. ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info Data from linked registries can contain information concerning health outcomes as well as risk factors. They are thus increasingly used in epidemiologic and clinical research, either as a stand-alone data source, or linked with data from pre-existing study populations. A more intensive use of these registries, especially for cohort studies, consists in linking registries prospectively for all eligible subjects, participants as well as non-participants. A cohort of nonparticipants has a high potential for handling possible selection bias that could occur among the cohort of participants. We will illustrate this design with the French "general-purpose" cohort Constances (www.constances.fr), which aims to recruit 200 000 volunteers starting January 2012, and is intended to serve as an "open epidemiologic laboratory" accessible to the wider epidemiologic research community. Through the social security number, databases from the national pension fund, from health insurance plans and from causes of death registries will be linked for both participants and non-participants. The large amount of information collected includes lifelong occupational history, recourse to general healthcare and hospitalization, medical procedures, etc. The optimal approach for handling selection bias consists in analysis-specific use of all available information, by doubly-robust methods for example. However, these may be too complex for routine use, due mainly to highdimensionality issues. A simpler alternative approach consists in using standard weights that correct for differential participation, but may in turn induce variance inflation or even bias amplification. We will present different weighting schemes we intend to develop, and discuss their potential advantages and disadvantages. P8 Evaluating hospital performance P8.1 Mixed effect models for provider profiling in cardiovascular healthcare context Francesca Ieva, Anna Paganoni Politecnico di Milano, Milano, Italy 95/156 Mortality following hospitalization is commonly used for the comparison of hospital quality. We define 30 days mortality for hospitals to be the number of all-cause-deaths occurring in- or out-of-hospital within 30 days, counting from first day of admission, among all patients. In June 2011, the Norwegian Knowledge Centre for the Health Services published results from a study assessing 30 days survival after admission for acute first time myocardial infarction (AMI) and cerebral stroke (CS) based on data from all Norwegian Hospitals. A logistic regression model was used for the estimation of 30 days adjusted mortality. In addition to hospital, the model included the case-mix variables: age, sex, seriousness of the medical condition, number of previous hospital admissions, and the Charlson comorbidity index. As mortality is considered a negative framing, the survival rates were presented rather than the mortality rates. The analyses identified statistically significant lower survival for some hospitals. One hospital turned out to have low survival rates for both AMI and CS. Further data analyses were undertaken including Kaplan-Meier curves for the survival of the 30-days period. The Kaplan-Meier curves revealed different survival patterns for the two patient groups. For AMI, the outlier hospital showed a drop in survival the first two days after admission compared the pooled results of the remaining hospitals. For CS, the drop was observed about 10 days after admission versus the pooled results. This information was used to inform a quality improvement project. P8.3 Accounting for patients transferred between hospitals when using mortality as a quality indicator for the comparison of hospitals Doris Tove Kristoffersen, Katrine Damgaard, Jon Helgeland Norwegian Knowledge Centre for the Health Services, Oslo, Norway Mortality is commonly used as a quality indicator for hospital comparisons. We define 30 days mortality for hospitals to be the number of all-cause-deaths occurring in- or out-of-hospital within 30 days, counting from first day of admission, among all patients. For patients transferred between hospitals a major challenge is to attribute the outcome (alive or dead) to each hospital. The objective of the present work was to evaluate a weighting method based on all hospital stays for transferred patients when calculating 30 days mortality. Consider a patient who stayed five days in one hospital, was transferred and stayed for two days in a second hospital, was transferred to a third hospital in which the patient died 10 days after admission. One approach is to include patients with a single hospital stay only (XM). We propose to use weights proportional to the length of stay in each hospital (WM); i.e. 5/17 for hospital no. 1, 2/17 for hospital no. 2 and 10//17 for hospital no. 3. This weighting provides mortality based on all admissions and all hospitals. The weighted outcomes add up to the total number of patients. Alternatively, the weights may be used in the model specification of a logistic regression. To compare XM and FM, we used data from a Norwegian nationwide allhospital sample for patients admitted for acute myocardial infarction, stroke and hip fracture. Spearman rank correlations were high for AMI (r=0.88) and stroke (r=0.80), but lower for hip fracture (r=0.67). The purpose of this work is to highlight how advanced statistical methods can be used to identify suitable models for complex data coming from clinical registries, in order to assess hospitals performances in treating patients affected by STEMI (ST segment Elevation Myocardial Infarction). We fit different models, trying to enhance the grouping structure of data for profiling aims, where the hospital of admission is the grouping factor for the statistical units (the patients). In these models we introduce performance indicators in order to adjust for different patterns of care as well as different observed casemix. In particular, we propose three methods to profile hospitals: in the first one we compare the in-hospital survival rates after fitting a Generalized Linear Model according to the so called Statewide Survival Rate (SSR); in the second one we fit a parametric Generalized Linear Mixed Effects Model on in-hospital survival outcome and a clustering procedure is then applied to the point estimates of hospital effects. Finally, in the third case we classify hospitals according to the variance components analysis of the random effect estimates, where nonparametric assumptions have been considered. The survey we consider for the case study is a clinical observational registry concerning patients admitted with STEMI diagnosis in any hospital of our regional district. The nearly unanimous agreement of results obtained P8.4 implementing the three methods on data supports the idea that a real clustering structure in groups exists. Such methods provide also a useful Performance of Screening Colonoscopy Centres in a Nationwide Colorectal Cancer Screening Programme: Evaluation Using Hierarchical Bayesian Model decisional support to people in charge with healthcare planning. Ondrej Majek1, Stepan Suchanek2, Miroslav Zavoral2, Ladislav Dusek1 1 Masaryk University, Insitute of Biostatistics and Analyses, Brno, Czech P8.2 Republic, 2Charles University, 1st Faculty of Medicine, Central Military Hospital, The use of Kaplan-Meier plots when comparing hospital mortality Department of Medicine, Prague, Czech Republic Doris Tove Kristoffersen, Katrine Damgaard, Ole Tjomsland, Jon Helgeland Czech National Colorectal Cancer Screening Programme was initiated in year Norwegian Knowledge Centre for the Health Services, Oslo, Norway 2000. Patients can undergo faecal occult blood test (FOBT) or primary 96/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info screening colonoscopy. Proportion of patients detected with adenoma at the FOBT+ follow-up colonoscopy - estimate of positive predictive value for detection of adenoma (PPV) - was previously proposed as an early performance indicator in colorectal cancer screening programmes using FOBT. Our objective was to assess possibility of identifying underperforming providers using hierarchical modelling with Markov chain Monte Carlo (MCMC) approach. The model was specified including centre-specific random effect. To adjust for case-mix of patients examined at individual centre, sex and five-year age group were added as patient-level covariates. PPV was modelled using logistic regression model using WinBugs package. The MCMC approach allows us to infer probability that the true value of PPV estimated at a particular centre reaches the nationwide mean value within all centres. Our study included 143 centres that recorded more than 50 colonoscopies in FOBT+ subjects performed in 2010. In total, 16,722 individuals underwent colonoscopy; adenoma was detected in 5,563 subjects. Overall PPV was 33.3%. Centre-specific ratio of odds for adenoma detection in comparison with nationwide mean ranges between 0.4 and 2.9. In 22 centres, the estimated probability of actually reaching the nationwide mean is below 5%, showing potential ineffectiveness in adenoma detection. The MCMC approach enables us to detect potentially underperforming centres while adjusting for known covariates, which may help programme management to address corrective actions and achieve continuous improvement in quality of screening services. P8.5 Hospital volume and survival from cancer surgery: the experience of a local area with an high incidence of gastric cancer Elisa Carretta1, Mattia Altini1, Paolo Morgagni2, Emanuele Ciotti3, Domenico Garcea2, Oriana Nanni1 1 Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST), Meldola (Forlì-Cesena), Emilia-Romagna, Italy, 2Department of General Surgery, GB Morgagni General Hospital, Forlì, Emilia-Romagna, Italy, 3Local Health Authority, Bologna, Emilia-Romagna, Italy Many studies have shown conflicting results on the relationships between hospital volume and survival of patients undergoing cancer surgical procedures. The objective of this study is to explore the influence of hospital volume on survival in patients undergoing gastric cancer surgery in the hospitals of a local area (Romagna) of the Emilia-Romagna Region (Italy). Hospital discharge records of patients admitted from January 2004 to December 2008, were linked with histological reports and regional mortality registry. Follow-up data were available until December 2011. Adjusted HRs of operative mortality and long-term survival in high and medium volume hospitals compare to low volume hospitals were estimated using shared frailty Cox regression model. Hospital volume ranged from 15-82 cases/year. The 3-year and 5-year overall survival for the 1096 patients identified in our cohort was 50% (95%CI:47-53) and 41% (95%CI:38-44) with a median follow-up of 69 months. Operative mortality decreases with increasing volume categories (p=0.047). Moreover, patients undergoing to high and medium volume hospitals had a significant improvement in long-term survival compared to those treated in low volume centers (HR=0.82, 95%CI:0.67-0.99; HR=0.77, 95%CI:0.62-0.95). Patient and tumor characteristics associated with long-term survival included: gender, age, comorbidity index, procedure type, T staging, number of positive lymph nodes and number of lymph nodes removed. The use of linked administrative databases and clinical records enables the examination of the relationship between hospital volume and survival after controlling for potential confounding. This methodological approach may help with planning organizational tools to improve patients outcomes and the quality of surgical services. P8.6 Tolerance intervals for identification of outlier healthcare providers: the incorporation of benchmark uncertainty. Sarah Seaton, Bradley Manktelow Department of Health Sciences, University of Leicester, Leicester, UK Emphasis is increasingly being placed on the reporting of healthcare provider outcomes with funnel plots, a standard graphical technique to identify providers with potentially outlying performance. The control limits for such plots are generally obtained by constructing prediction intervals around an underlying ‘benchmark' and providers that fall outside of these limits are seen as potential outliers. However, such benchmarks are usually obtained from observed data, for example the ‘average' outcome across all providers. The conventional use of prediction intervals ignores any statistical uncertainty associated with the estimation of the benchmark. An alternative method derived from Statistical Process Control (SPC), tolerance intervals, allows the inclusion of this uncertainty. The construction of tolerance intervals comprises 2 steps: first, a confidence interval is calculated for the benchmark; second, prediction intervals are then calculated using the upper and lower bounds of the confidence interval as the benchmarks for the upper and lower limits respectively. Tolerance intervals are always wider than the corresponding prediction intervals due to the additional uncertainty incorporated from the confidence interval created in the first step. Therefore the use of tolerance intervals will reduce the probability of falsely identifying a provider which has an underlying performance equal to the benchmark as an outlier. Examples of tolerance intervals will be provided using simulated and real data. Tolerance intervals should be used on funnel plots when the benchmark is estimated with uncertainty. These limits incorporate the statistical uncertainty from the estimated benchmark and provide more robust method for the identification of outliers. P8.7 Geographic variations in avoidable hospital admissions for asthma across Germany Maria Weyermann, Saskia E. Drösler, Ann-Kathrin Weschenfelder, Silke Knorr Niederrhein University of Applied Sciences, Krefeld, Germany Background Ambulatory care for chronic conditions was assessed by adult hospital admission rates across 22 countries within the OECD Health Care Quality Indicators (HCQI) Project. We calculated asthma admission rates across all 16 Federal States of Germany and investigated possible associations with variations in health care supply expressed as density of hospital beds and rates of ambulatory care providers (general practitioners and internists per population). Methods Using the 2009 nationwide Diagnosis Related Groups statistic provided by the National Statistical Office we calculated age-sex standardized asthma admission rates according to the OECD HCQI Data Collection Guidelines. Results Standardized asthma admission rates ranged from 11.5 (Berlin) to 26.3 (Saxony-Anhalt) admissions per 100.000 population. Pearson's correlation coefficients indicated only weak associations with provider rate (r: -0.48028; p: 0.0597) and hospital beds (r: 0.57145; p: 0.0208). After mutual adjustment both associations became stronger (provider rate adjusted for hospital beds density: partially adjusted r:-0.74872; p: 0.0013; hospital beds density adjusted for provider rate: partially adjusted r: 0.78444; p: 0.0005). Further adjustment for school education and asthma prevalence decreased correlation between asthma admission rate and provider rate (partially adjusted r: -0.67011; p: ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info 0.0122), but further increased correlation between asthma admission rate and hospital beds density (partially adjusted r: 0.88359; p: <.0001). Sex-specific analyses showed similar patterns. Conclusions Admission rates calculated on hospital administrative data might be a useful tool to identify regional variations of asthma hospitalizations. To some extent variations in health care supply explain divergences. 97/156 indirect standardisation may not be correct because different weights are used for each comparator. We propose an alternative method of standardisation where event rates are calculated in risk groups rather than casemix groups. The complex multidimensional casemix is converted into a simple one-dimensional risk distribution using a logistic regression model and then the events are directly standardised across the risk distribution. This method is illustrated using the Summary Hospital-Level Mortality Index (SHMI) for English hospital trusts. P8.8 The comparative evaluation of Italian Regional Health System through PLS- P8.10 SEM Probability of in-hospital mortality: analysis of administrative data in Germany Federico Ambrogi, Monica Ferraroni, Adriano Decarli Silke Knorr, Saskia Drösler, Maria Weyermann University of Milan, Milan, Italy Niederrhein University of Applied Sciences, Krefeld, Germany The National Agency for Regional Health Services (AGENAS) promoted a project with the goal of evaluating the performances of the Italian Regional Health Systems using 102 health indicators selected by a group of experts. In this work Structural Equation Modeling (SEM) estimated by Partial Least Squares (PLS), was used to analyze the cause-effect relationship between the latent variables summarizing the health system functioning and composed by the different indicators. Each regional indicator is supposed to enter only one of the defined 10 latent construct, namely: availability of the resources; sociodemographic conditions; average health status; exploitation; expenditure; costs; quality of care; hospital use; effectiveness; efficiency. In the structural reflective outer model, the first three dimensions were considered as exogenous explaining differences in the exploitation of the resources, which directly influence costs and indirectly through expenditure. The costs influence the quality of care and hospital use, also affected by the average health status. At the end of the causal path, quality of care and hospital use affects effectiveness and efficiency of the system. A negative association between costs and hospital use (r = -0.41) and quality of care (r = -0.662) was estimated. Hospital use and quality of care are then negatively associated with effectiveness and efficiency which were finally used for the comparative analysis of the regional systems. The approach seems to be useful in conceptualizing a framework for the health system, although created starting from the considered indicators, producing a synthesis of the available information incorporating a causal model. P8.9 Casemix adjustment for comparing standardised event rates Richard Jacques, James Fotheringham, Michael Campbell, Jon Nicholl School of Health and Related Research, University of Sheffield, Sheffield, UK In all branches of the health and social sciences we need to be able to compare outcomes of groups of patients or people managed in different ways to understand the impact of different interventions, services and policies. Fair comparison of outcomes can be difficult to achieve because of differences in the characteristics of the patients and populations served. Distribution of these characteristics is known as casemix, and when the casemix is associated with the outcomes, comparisons of outcomes are confounded with any differences in casemix. In theory this problem can be solved by adjusting the comparison for casemix. This can be done by calculating a standardised event rate. The two most commonly described methods of adjusting for casemix are direct and indirect standardisation. However, comparisons made using direct standardisation may not reflect the true performance of the comparators because of different patterns of random zeros (e.g. for some rare conditions there may be no cases in some hospitals in some years) or organisational zeros (e.g. some hospitals don't treat children), and comparisons made using Background In-hospital mortality is the basis of established quality indicators for hospital care such as Hospital Standardized Mortality Ratios (HSMR). As administrative hospital data include information on discharge status, we aimed to calculate the probability of in-hospital mortality using various measurements for comorbidity. Methods According to the HSMR methodology of the Canadian Institute for Health Information (CIHI) we investigated the probability of in-hospital mortality using the 2008 nationwide G-DRG statistics (17 mio hospitalizations). Case selection in accordance to CIHI reduced the population to 5.2 mio hospitalizations. In logistic regression models mortality risk was calculated using age, sex, hospital transfer, length of stay, admission category, primary diagnosis and comorbidity measurements (Charlson-, Elixhauser-Index, mean number of secondary diagnoses) as independent variables. Results Adjusted odds ratio for in-hospital mortality was 0.96 (95%CI: 0.95-0.96) for men compared to women, 1.53 (95%CI: 1.51-1.55) for patients transferred vs. non-transferred, and 1.94 (95%CI: 1.93-1.96) for emergency compared to elective admission. Mortality risk increased with age (OR: 1.05 per year; 95%CI: 1.05-1.05) and comorbidity (OR: 1.21 per Charlson-Index unit; 95%CI: 1.21-1.21). Compared to length of stay 3-9 days, odds ratios ranged from 1.02 (95%CI: 1.02-1.04 - 10-15 days) to 2.62 (95%CI: 2.6-2.65 - 1 day). Different comorbidity measurements did not reveal substantial changes in regression coefficients or c-statistics. Conclusions The HSMR methodology of the CIHI can be applied on German DRG data and the results are comparable to Canada. The choice of comorbidity measurement models seems to be negligible. P9 Functional data analysis P9.1 Visualisation and spatiotemporal smoothing of single trial EEG data Stanislav Katina2, Igor Riecansky2 1 The University of Glasgow, Glasgow, Scotland, UK, 2Laboratory of Cognitive Neuroscience, Institute of Normal and Pathological Physiology, SAS, Bratislava, Slovakia, 3SCAN Unit, Institute of Clinical, Biological and Differential Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria For electroencephalographic (EEG) spatial data (scalp voltage measurements), currently applied methods partially or completely ignore spatial relationship of 3D coordinates of the electrode location called (semi)landmarks. The coordinates are often projected to 2D and used only for visualisation in the plane and their covariance structure is not incorporated into subsequent multivariate statistical modelling. On the other hand, the landmarks contain 98/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info only a very small proportion of data available. However, curves and surface patches have the advantage of providing a much richer expression of head shape. In the talk, this is explored in the context of using principal curves and the Pspline approach in smoothing to construct a set of semilandmarks on curves in between electrode locations at any desired resolution. Interpolation thin-plate spline model is then used to estimate EEG-signal in these artificial electrode positions. Resulting signal is spatially smoothed and visualised as coloured animated 4D spatiotemporal maps. The methods are illustrated on an application with real data, where the EEGsignal was recorded from 61 scalp sites using sintered Ag-AgCl electrodes mounted on an elastic cap (EASYCAP GmbH; Herrsching, Germany) and electrode positions were determined using photogrammetric head digitizer. P10 Health economics and regulatory affairs P10.1 Bayesian Evidence Synthesis in a Health Economic Model for Dementia Jasdeep. K Bhambra, Fiona. E Matthews, Christohper. H Jackson MRC Biostatistics, Cambridge, UK Dementia is a group of disorders that describe a pathological state of cognitive impairment. Current drug treatments for dementia are intended to improve cognitive function. To compare long-term effects and costs of different treatments, models are typically built which describe patients' transitions between states of health and cost-incurring events. At present, a publicly accessible, fully comprehensive model for patients with dementia is not available. We are currently building such a model, an important part of which is estimating progression in cognitive function from diverse sources of published data. Evidence synthesis methods are used to estimate progression rates between stages of cognition in a Markov model. We examine how these vary with age, sex, education, comorbidities and settings of care. Different published studies have used a variety of models to estimate cognitive progression, which is then expressed in a variety of ways. Such models include survival-type models for times until cognition declines to a particular threshold, and Markov models for progression through disease stages. Our methods will need to combine these diverse estimates of disease process. Bayesian methods are used to model between-study heterogeneity in progression probabilities and the effects of treatment and other covariates on them. We discuss uncertainties about the model structure, prior distributions and the relevance of each source of data, and examine these using sensitivity analyses. Directions for future research will be discussed, including the importance of ensuring that the model is flexible to include not only the patients changes but also the impact on their careers. phase II/III clinical trial in patients with symptomatic malignant ascites comparing paracentesis plus catumaxomab (N=170) with paracentesis alone (N=88) were analysed using this approach. Health-Related Quality of Life (HRQoL) was assessed using the EORTC QLQ-C30, a PRO instrument specifically designed to measure HRQoL of cancer patients. Meaningful deterioration in the 15 QLQ-C30 scores was defined as a decrease in score of at least 5 points. Kaplan-Meier estimates with log-rank test and Cox models adjusted for baseline score, country, and primary tumor type were used to analyse time-to-HRQoL-deterioration. Results: Meaningful deterioration in HRQoL scores appeared more rapidly in control than in the catumaxomab group (medians: 16-28 days vs. 45-49 days). The difference in time to first deterioration in HRQoL between groups was statistically significant for all 15 QLQ-C30 scores (p<0.05) and results were confirmed using Cox models (p<0.05 for all scores). Conclusion: Time to PRO response is a useful approach to obtain meaningful results from PRO data with respect to HRQoL outcomes. P10.3 Prediction of pregnancy outcomes in planned homebirth Dorota Doherty1, Jeff Cannon1, Janet Hornbuckle2 1 Women and Infants Research Foundation, Perth, Western Australia, Australia, 2 King Edward Memorial Hospital for Women, Perth, Western Australia, Australia, 3School of Women's and Infants' Health, University of Western Australia, Perth, Western Australia, Australia The debate on safety of planned home birth continues in literature, policy and practice across the developed world. Main concerns include increased perinatal mortality and excess morbidity for women and their babies who require intrapartum or postpartum transfer with planned homebirth. The difficulty of evaluating the outcomes in planned homebirth relates mainly to the low numbers of homebirths that occur in the local obstetric population. This difficulty is confounded by the demographic characteristics of women who elect homebirth that differ from the characteristics of women with planned hospital birth. We have developed a model simulating a pregnancy cohort that reflects maternal characteristics, pregnancy complications and pregnancy outcomes in our local pregnancy population. The model is constructed as an individual sampling model that uses Monte Carlo simulations to generate the events and outcomes for individual pregnancies. Up to two pregnancy complications are allowed to occur at any pregnancy week. Transition probabilities for complications and/or labour are estimated using logistic regression function including maternal characteristics, pregnancy complications and current pregnancy week. When complications occur, transfers of care are modelled according to the current clinical management guidelines. Our pregnancy model is used to estimate the rates of adverse outcomes associated with intrapartum and postpartum transfers into hospital care, maternal morbidity and perinatal mortality. The method of pregnancy simulation offers distinct advantages over alternative evaluation strategies because it converts the P10.2 crossectional pregnancy data into a longitudinal cohort. This method is Analysis of time to patient-reported outcome meaningful change: Illustration particularly useful for estimation of transfers into hospital care without from a clinical trial with catumaxomab in patients with malignant ascites conducting actual clinical studies. Hélène Gilet, Antoine Regnault Mapi Consultancy, Lyon, France P10.4 Background: In many Patient-Reported Outcome (PRO) application contexts, the meaningful question is not whether a change in the PRO can be observed but how fast the change occurs. In such case, it is possible to build on the approach currently recommended to support PRO interpretation, analysis of PRO response (i.e. individual patient PRO score change that can be interpreted as meaningful). Indeed, survival analysis techniques can be applied with PRO response as the event of interest. Methods: PRO data collected in a 2-arm, randomized, open-label, multicenter, Multivariate latent class model for non-supervised classification in RNAseq experiments Juan R Gonzalez, Mikel Esnaola Center for Research in Environmental Epidemiology (CREAL), Barcelona, Spain High-throughput RNA sequencing (RNA-seq) offers unprecedented power to capture the real dynamics of gene expression. So far, statistical methods to analyze this data are based on detecting genes that are differentially ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info 99/156 expressed among different conditions. However, large consortia like TCGA or International Cancer Genome Consortium are generating a vast amount of RNA-seq data to address other biological questions. Some researchers have paid attention to detect sub-groups of individuals with similar genetic profiles. Others are interested in elucidating whether patients show patterns due to nonbiological differences like batch effects. Those problems can be addressed using non-supervised methods (NSM). The main difficulty when addressing this problem is that existing NSM are based on normality assumptions. However, RNA-seq data are counts, and hence, other distributions like Poisson, Negative Binomial or Poisson-Tweedie should be used instead. In this talk, we will present how to perform non-supervised classification using a multivariate latent class model based on count data distributions. We will also show how to incorporate correlation among genes using genomic information (derived form gene ontolgy databases) in order to improve accuracy. Through simulation studies and using real data sets belonging to TCGA consortium we will compare our proposed method with other existing ones. Finally, an R package implementing our proposed model will be presented. in weight loss, models a hypothetical two arm trial. Measurements are taken at baseline and end of the trial. Final measurements are generated in 3 ways: from a Normal distribution, from a t-distribution and from a Normal distribution with extreme outliers added. The parameters in the selection process are chosen to create missing data at different levels of non-ignorability. The RAMs used to analyse the simulated data assume all measurements are normally distributed. RAMs are found to perform reasonably well when data are simulated under a Normal distribution or a t-distribution. Although RAM models appear to handle model misspecification better than selection models, they also seem to be sensitive to outliers, which introduce severe bias to parameter estimates. RAMs capture more information about non-respondents and help to fit MNAR selection models. P11.1 Can the repeated attempts model help to fit MNAR selection models? Danice Ng, Dan Jackson, Ian R. White MRC Biostatistics Unit, Cambridge, UK Background: Inference from longitudinal studies, with unit missingness, is prone to bias if the underlying missingness process is not accounted for during analysis. Dataset with baseline and a repeated measure was derived from the 1958 National Child Development Study. Various regression methods applied to the data under the assumptions of MCAR, MAR, MNAR. Method: Inference was estimating the odds of cases having headaches in adulthood given that they had headaches at childhood. Dataset had 6,753 subjects at baseline and 5,953 at the second time point. The GEE (independent) model applied when missingness process assumed to be MCAR; weighed GEE (independent) model with inverse probability weights when S-MAR; marginalised transition model (MTM) when MAR; a modified GEE model (Fitmaurice et al, Biostatistics 2000) and a regression model analysed with MCMC Gibbs sampling when MNAR. Results: Odd ratios (95% CI) of cases having headaches in adulthood if they had headaches at childhood at ages 7 or 11 obtained under the missingness assumption of MCAR is 1.780 (1.499,2.114); S-MAR is 1.818 (1.566,2.109); P11.2 Using planned missing values in longitudinal trials to relieve patient burden and reduce costs Christele Augard1, Ayca Ozol-Godfrey2, Robert D. Small2, Dominika P10.5 Wisniewska2 The (little) need for and the (large) impact of post hoc application of formal 1Sanofi Pasteur, Marcy l'Etoile, France, 2Sanofi Pasteur, Swiftwater, PA, USA criteria to check clinical relevance in well conducted RCTs Some longitudinal trials require subjects to commit to frequent blood draws at Werner Vach, Primrose Beryl visits over time. Often the primary endpoint does not require the observations Clinical Epidemiology, Freiburg, Germany from every visit. This is true, for example, in vaccine immunogenicity trials and Recently, the topic to address clinical relevance on the top of statistical diabetes trials. Taking samples at every visit can be burdensome to both the significance in the analysis of RCTs has been paid increasing attention. The subject and the sponsor. Subjects often do not like many blood draws. The cost increased interest is related to the fact that in political decisions on of assaying every sample can be high. These facts contribute to increased cost reimbursement a pure proof of efficacy is no longer regarded as sufficient, but and subject drop out. In this paper we investigate the idea of bleeding random that a clear clinical benefit has to be proven. In this paper we would like to subsets of subjects at each visit but using the (frequent) high correlation within quantify the need for and the impact of post hoc application of formal criteria to subjects between visits to build imputation models to implement an MI assess clinical relevance, if applied in well conducted, single RCTs. We focus approach to analyzing the data. We use the observations present as well as on assessment of clinical relevance based on the global treatment effect, which other pertinent continuous and categorical variables to build the models. We do is the method of choice if response cannot be defined at the individual level. the estimation of the imputation models using a method of Raghunathan, The two criteria we consider are the comparison of the lower bound of the two- Lepkowski et.al. (sequential regression procedure) which is very general and sided 95% CI for the treatment effect with a pre-specified threshold - which is can handle many variable types. We give examples using data from some equivalent to testing a shifted null hypothesis - and the comparison of the vaccine trials. We show how various patterns can reduce cost and possibly treatment effect with a pre-specified threshold. Our results suggest that there is drop outs. little need for a formal assessment, if the irrelevance limit is lower than 25% of the effect assumed in the power calculation. Furthermore, in most situations a P11.3 possible gain in controlling the rate of accepting a new treatment with an irrelevant effect is outperformed by the loss in power to accept treatments with Regression models for repeated binary measures under different missingness assumptions relevant effects. Bola Coker King's College London, London, UK P11 Incomplete data MNAR models are useful when the assumption that data are MAR does not seem plausible. However, MNAR selection models are very sensitive to possible outliers and/or modelling assumptions. One way to gain information about the non-respondents is to make repeated attempts to obtain outcome data. Non-respondents are believed to behave more similar to laterespondents. Since details on the number of attempts might be able to provide more information about ignorability of non-response, they might be profitable in overcoming difficulties encountered when estimating MNAR selection models. A simulation study is performed to investigate the value of MNAR models in conjunction with information on number of attempts, namely the repeated attempts models (RAMs). The simulation study, motivated by a randomised trial 100/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info MAR is 1.774 (1.540,2.042); MNAR - pattern mixture model is 1.784 (1.547,2.052); MNAR - selection model is 1.7916 (1.584,2.026). Conclusion: All models applied were relatively unbiased in providing odd ratio estimates. The different methods required assumptions. The S-MAR assumption required the predictive probabilities; MTM model required the serial dependence of the variables; while the MNAR models required the distribution of the dropout pattern. Further work required to optimise predictive and missingness models with more repeated readings and different percentages of dropouts. measures of discrimination and calibration; either complete case (CC) or multiple imputation (MI) being preferable approaches. However it is unclear whether MAN is inferior to CC and MI when the true missing data mechanism is close to MAN. 2000 datasets with n=1500 were simulated using a logistic relationship between binary outcome and four binary risk factors. Five, 15, 30 and 50 percent of the values of risk factor X1 were set to missing under three different scenarios: MAR, "very nearly" MAN, "nearly" MAN. Under a nearly MAN missing data mechanism, using MAN to deal with missing data led to bias in estimates of X1's odds ratio. Further, MAN under-estimated the model's discrimination ability. CC led to bias in the assessment of P11.4 calibration performance. MI exhibited least bias for model coefficients, Drop out in randomized controlled non-inferiority trials with time to event discrimination and calibration. outcome: a worst case sensitivity analysis using a Bayesian method Even when the true missing data mechanism is nearly MAN, using MAN as the Ákos Ferenc Pap method for dealing with missing data in development of risk prediction models Bayer Pharma AG, Wuppertal, Germany is not preferable to using MI. Additionally, CC is not preferable to MI in this situation. Motivation The EMA Guideline on Missing Data in Confirmatory Clinical Trials suggests a worst case analysis: assigning the best outcome to missing values (drop outs) in the control group and the worst outcome to those of the experimental group. Background - Study design: randomised-controlled trial, parallel group - Study outcome: occurrence of prespecified event - Scientific hypothesis: the experimental treatment is non-inferior to the active reference treatment - Non-inferiority margin: determined as preservation of a specific fraction of the effect (hazard ratio) of the reference treatment versus placebo/no treatment determined by meta analysis. - The primary analysis is done by fitting Cox proportional hazards regression model assuming that all censoring all non-informative. Issue Some patients drop out before the end of the study and informative censoring may be questionable. Sensitivity analysis is needed to assess the impact of the extent of drop out on the primary analysis. Sensitivity analysis The drop out status* treatment group interaction term is included in the Bayesian proportional hazards model as binary variable using Proc Phreg in SAS version 9.2 (by MCMC method). For all regression coefficients flat normal priors were defined except for the drop out status* treatment group interaction term. For this term the effect of the reference treatment over placebo from the meta analysis was defined as prior assuming that among drop outs the experimental treatment was equivalent to placebo/no treatment. Worst case analysis scenarious to obtain posterior estimates of the hazard ratio for the overall treatment effect from the above model will be presented. P11.6 Contrasting Informative Cluster Size with Missing Data Menelaos Pavlou1, Shaun Seaman2, Andrew Copas3 1 University College London, London, UK, 2MRC Clinical Trials Unit, London Hub for Trials Methodology Research, London, UK, 3MRC Biostatistics Unit, Cambridge, UK When making marginal inference for clustered data with varying cluster size, three populations are of potential interest. Firstly, there is the population of all members of clusters. Secondly, there is the population of typical members of clusters. Inference for these first two populations is different if cluster size is informative. Thirdly, if the variation in cluster size has arisen because of missing data, we may view the observed clusters as incomplete and seek inference for the population of all members of complete clusters. Alternatively, cluster-specific inference can be sought, using random effects models. We clarify that if the random-effects model is correctly specified, there is no distinction between cluster-specific inference for all members and clusterspecific inference for typical members. Missing data methods are well known by statisticians; methods for informative cluster size (ICS) are less well known. Previous authors have vaguely referred to the relation between ICS and missing data mechanisms (MDMs). We clarify this relation and investigate which MDMs may lead to ICS. We show that when within each complete cluster each member has the same probability of being missing, inference for typical members of clusters and inference for all members of complete clusters are equivalent. We survey the methods for inference concerning the observed and complete clusters, and explain why different methods are needed for the two. We describe how the methods which view the observed clusters as complete can nevertheless be seen as special cases of methods for (hypothetical) missing data. P11.5 Dealing with missing data in the development and validation of clinical risk prediction models: is missing as normal ever a sensible strategy? Rory Wolfe1, Roman Ahmed1, Gareth Ambler2 1 Monash University, Melbourne, Australia, 2University College London, London, UK P11.7 Multiple imputation in a longitudinal cohort study: a case study of sensitivity to imputation methods Helena Romaniuk1,2,3, John B. Carlin1,2 1 Clinical Epidemiology & Biostatistics Unit, Murdoch Childrens Research Institute, Melbourne, Victoria, Australia, 2Department of Paediatrics and School of Population Health, University of Melbourne, Victoria, Australia, 3Centre for Adolescent Health, Murdoch Childrens Research Institute, Royal Children’s Hospital, University of Melbourne, Victoria, Australia In development of risk prediction models missing risk factor data are sometimes assumed "missing as normal" (MAN). For example, if a laboratory test has not been ordered it might be assumed that the true test result would have been in the "normal" range. If the true missing data mechanism is missing at random (MAR) then employing MAN when developing risk prediction models Multiple imputation for handling analysis of incomplete data has achieved can be shown to introduce severe bias in estimates of model coefficients and widespread use over the past decade, and has been used extensively by the ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info authors on a large longitudinal cohort study, the Victorian Adolescent Health Cohort Study. Although we have endeavoured to follow best practice according to current literature, we have to date performed limited examination of the extent to which variations in our approach might have led to different results in the final substantive analyses. In applying multiple imputation the user makes a large number of decisions about the method of imputation, whether explicitly or implicitly (by accepting default options in software) and there is little published advice to guide practice. We have examined sensitivity of analytic results to decisions about imputation method in the context of an analysis on the history of illicit substance use in the cohort that has been published elsewhere 1. Key factors investigated included: impact of imputation method (mi impute mvn and ice in Stata 11); inclusion of auxiliary variables; omission of cases with too much missing data; approaches for imputing highly skewed continuous distributions that are analysed as dichotomous variables. We found that MVNIbased estimates were consistent across the different approaches, while ICE estimates were more susceptible to decisions made. Importantly, estimates of association parameters were less sensitive to imputation model settings than estimates of prevalence parameters. 1 Swift W et al. Cannabis and progression to other substance use in young adults: findings from a 13-year prospective population-based study. JECH 2011, doi:10.1136/jech.2010.129056. 101/156 frailties. A wide variety of frailty models and several numerical techniques have been mentioned in the literature. Herein, we investigate the performance of frailty variance estimates in a repeated events data scenario. Our study was motivated by post kidney transplantation records of 467 patients (at the Academic Medical Center, Amsterdam) who experience repeated infections of different types, and were also repeatedly measured for five biomarkers. In a simulation study, we postulated the association between the biomarkers and the infection times to be according to a Cox proportional hazards model. Two biomarkers were generated by assuming a bivariate random effects model. An infection-specific Cox proportional hazards model with log normal frailties was fitted to each generated data set. A sample size of 300 patients was considered. We allowed the infection specific hazards to be influenced by timedependent true biomarker values. Findings revealed that although the estimated variances were on average close to the true values, the true and the estimated variances had only little correlation (0.08) across 250 simulations. This disparity reduced with increasing sample and cluster sizes. Also, estimated variance parameters were more skewed and less spread out. The true frailty standard deviation was better estimated by the square root of the mean of the simulated variances than their standard deviations. These findings had no significant effect on the inference of other parameter estimates. P12.2 P11.8 Bayesian methods for joint modelling of longitudinal and survival data to assess validity of biomarkers in AIDS data Multiple imputation for an incomplete covariate which is a ratio 1 2 1 Chiara Brombin, Clelia Di Serio, Paola M. V. Rancoita Tim Morris , Ian White , Patrick Royston 1 2 MRC Clinical Trials Unit, London, UK, MRC Biostatistics Unit, Cambridge, UK University Centre for Statistics in the Biomedical Sciences (CUSSB) VitaSalute San Raffaele University, Milano, Italy In medical applications, regression analyses often include the ratio of two variables as a covariate. Common examples include body mass index The statistical analysis of observational data arising from HIV/AIDS research is (BMI=weight÷height^2) and the ratio of total to HDL cholesterol. If one generally tricky since both longitudinal (repeated measurement) as well as component is missing, the ratio is missing. Incomplete covariates are often survival (time-to-event) data are available. dealt with by multiple imputation. One strategy for imputing ratios is to impute These outcomes are often separately analyzed. However, in many instances, a the components and then calculate the ratio from the two imputed values joint modeling approach is required to produce a better insight into the (passive imputation). Alternatively, one might impute the ratio directly, ignoring mechanisms underlying the phenomenon under study. observed values if either component is missing (active imputation). Following the approach proposed in Guo and Carlin (2004), in this paper we fit `Congeniality' describes the relationship between the model for analysis and a fully Bayesian joint model to analyse epidemiological data on HIV patients the model for imputation; they are congenial if a full probability model exists enrolled in the CASCADE (Concerted Action on Sero-Conversion to AIDS and which accommodates both conditional models. When considering the Death in Europe) Study. imputation of ratios, the passive strategy outlined above is uncongenial; the Actually CASCADE is one of the largest AIDS multicentre studies and Qrisk cardiovascular risk score is a high profile example of passive imputation represents a collaboration between the investigators of 22 cohorts in 10 going wrong. Meanwhile the active strategy is congenial, as are strategies European countries, Australia and Canada, aiming at monitoring newly infected which also impute one/both component/s alongside the ratio. It is unclear what individuals and those already enrolled in studies, covering the entire duration of the impact of uncongeniality on parameters of interest is in real datasets. HIV infection. Using two example datasets, one with incomplete BMI and one with incomplete The focus will be on the Italian cohort. We take advantage of the quality of cholesterol ratio, various approaches to dealing with the incomplete ratios these data to explore alternative models for the joint distribution of the values via multiple imputation and fully Bayesian methods are illustrated. longitudinal data on CD4 cell count and RNA viral load and on the Differences between passive and active approaches can be surprisingly small event/survival data. Instead of survival time, we will concentrate on time to for BMI but are fairly large for cholesterol ratio. We recommend active seroconversion. imputation because it is more difficult to get wrong, no less efficient and WinBUGS package will be used and our results will be compared to those congenial. obtained using R package JM. The joint Bayesian approach appears to offer significantly improved and enhanced estimation of median survival times and other parameters of interest, as well as simpler coding and comparable P12 Joint modelling of outcome and time-to-event runtimes. Extension to competing risks is also considered. P12.1 A Simulation Study to Investigate The Performance of Frailty Variance P12.3 Estimates in Repeated Events Data Unfortunately, this poster has been withdrawn. Z.J. Musoro, R.B. Geskus, A.H. Zwinderman Academic Medical Center, Amsterdam, The Netherlands P12.4 In the analysis of event history data with multiple events of the same type, the dependency between associated events is often quantified via the use of Bayesian Modelling of Biomarker Data to Predict Clinical Outcomes 102/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info Keith Abrams1, Michael Crowther1, Paul Lambert2 P13 Latent variable models 1 University of Leicester, Leicester, UK, 2Karolinska Institutet, Stockholm, P13.1 Sweden A Bayesian multivariate multilevel probit model applied to nursing burnout data Biomarkers are increasingly being used to both detect disease earlier than Baoyue Li1, Luk Bruyneel2, Walter Sermeus2, Koen van den Heede3, Kenan would otherwise be clinically possible, and to monitor treatment efficacy. In Matawie4, Emmanuel Lesaffre1,5 both situations the key aim is to be able to predict, with associated uncertainty, 1 Rotterdam, The Netherlands, 2University of Leuven, Leuven, a future clinical event based upon longitudinal biomarker profiles. Thus, a joint ErasmusMC, 3 Belgium, Belgian Health Care Knowledge Centre, Brussel, Belgium, modelling approach is often adopted in which a multivariate longitudinal model 4 5 for the biomarker data and a time-to-event model for the clinical outcome are University of Western Sydney, Sydney, Australia, Katholieke Universiteit Leuven, Leuven, Australia linked using shared random effects. The use of a Bayesian approach, implemented using Markov Chain Monte Carlo (MCMC), to such a joint BACKGROUND: Burnout among nurses is a major problem in hospitals and modelling framework has a number of advantages including; the flexibility that negatively affects patient care. In the multi-country RN4CAST study, burnout such an approach offers in terms of the complexity of both the longitudinal and was questioned to more than 30,000 nurses, from about 2,000 nursing units in time-to-event models, the ability of make/obtain (conditional) predictive about 400 hospitals from 12 countries. Three dichotomized measurements of statements/distributions, and the inclusion of external evidence, especially burnout were captured, as well as three nurse working environmental regarding the correlation over time of multiple biomarkers. These particular measurements. Previous research showed a significant association between features of the Bayesian approach will be illustrated using data on a cohort of the two kinds of measurements based on simple regressions. We questioned 4,834 diabetic patients who had Body Mass Index (BMI), serum cholesterol here whether these associations remained the same across all levels and and diastolic/systolic blood pressure measured repeatedly whilst being similarly for the relationship among the three burnout outcomes. Therefore, on followed up for the development of cardiovascular disease (myocardial top of the mixed effects mean structure, we added a mixed effects structure in infarction and/or stroke) and/or death. the correlation matrix. OBJECTIVES: We propose a Bayesian tri-variate four-level probit factor model to estimate the relationship between the three burnout outcomes and the P12.5 working environmental variables in each level, as well as a flexible correlation Causal effects of Total Antioxidant Capacity intake on risk of postmenopausal structure via a common latent factor with structured loadings. MCMC breast cancer in a cohort study computations were done using WinBUGS 1.4.3. Daniela Mariosa1, Weiwu Wang1, Weimin Ye1, Rino Bellocco2 RESULTS: Despite the complex structure of the data, all parameters were well 1 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, estimated. We obtained significant negative relationships between the working Stockholm, Sweden, 2Department of Statistics, University of Milano Bicocca, environment and the burnout variables in each level, with different magnitude. Milano, Italy Further, we found a positive correlation structure varying across countries but it Introduction: Established risk factors for postmenopausal breast cancer remained quite stable across hospitals and nursing units within a country. include alcohol consumption and body fatness, but the existence of additional CONCLUSIONS: The multivariate multilevel probit factor model provides an mechanisms between diet and onset of breast cancer cannot be ruled out. The elegant manner to flexibly model the multivariate binary data in a multi-level associations between different antioxidants and postmenopausal breast cancer context. The extension to categorical, ordinal and mixture outcomes presents risk are inconsistent and the hypothesis of a synergistic effect encourages to no difficulties. consider total antioxidants. Objectives: To apply causal inference methods to assess potential effects of P13.2 dietary total antioxidant capacity (TAC) intake on postmenopausal breast Using mixture models for identification of typical trajectories of recovery in cancer risk. patients with Major Depressive Disorder Methods: 19 051 women who were recruited into the Swedish National March Klaus Groes Larsen Cohort in 1997 filled out a 106-item food frequency questionnaire, from which TAC, in terms of total radical-trapping antioxidant parameter (TRAP) or ferric H. Lundbeck A/S, Copenhagen, Denmark reducing antioxidant power (FRAP), was calculated using published databases. The clinical relevance of the effect size of antidepressants in clinical Phase IIIOccurrence of cancer was ascertained through the Swedish Cancer Register. IV trials is under endless discussion. While there is general agreement that Follow-up was between October 1, 1997 or onset of menopause (if many antidepressants have an effect on depression symptoms, a combination premenopausal at enrollment), and occurrence of any malignant cancer, loss to of commonly seen large placebo effects and relatively modest drug-placebo follow-up, or end of the study (December 31, 2010), whichever came first. Cox differences make it difficult to make a clinically meaningful quantification of the proportional hazards regression models were employed to estimate adjusted true effect of the drug. hazard ratios (HRs) and 95% confidence intervals (95%CIs). We use data from a large database on placebo-controlled studies in Results: During 193 008 women-years, 606 cases were reported. TAC intake, depression including the antidepressant escitalopram and estimate subgroups in terms of FRAP or TRAP, was not associated with risk of postmenopausal that benefit differently from treatment. We obtain an interpretation that is breast cancer (highest vs lowest quintile, FRAP, adjusted HR=0.90, 95%CI completely different from the traditional ‘average effect' from linear regression 0.67-1.20, p-value for trend = 0.260; TRAP, adjusted HR=0.94, 95%CI 0.70- analysis. The mixture model allows for the identification of the proportion of 1.25, p-value for trend = 0.332). patients in each of two or more subgroups), as well as their mean improvement Conclusions: This study does not report an effect of dietary TAC on risk of in symptoms score during the trial. Baseline variables may be incorporated into the model, either as covariates predicting subgroup membership, or as direct postmenopausal breast cancer. effects on the observed variable (the symptom score). Mixture models have only recently started to appear in analyses of CNS trial data, and they are not without difficulties or even controversies. One notable issue is the estimation of the number of subgroups, which is central for the ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info interpretation of the results. We compare findings from our analysis with results and conclusions in the sparse literature on mixture models in depression and discuss the usefulness of the model framework for learning and understanding the effects of antidepressants. P13.3 Assessing Item Properties of the Hospital Anxiety and Depression Scale (HADS) for the Detection of Depression in Stroke Patinets using Item Response Theory (IRT) Salma Ayis, Luis Ayerbe Garcia-Mozon King's College London, London, UK The Hospital Anxiety and Depression Scale (HADS) is a screening questionnaire for the detection of anxiety (7 items) and depression (7 items) among hospital patients but widely used for general populations. Studies including systematic reviews that investigated the scale highlighted a range of cut off points to identify caseness based on a summation score that treats items equally. To understand the reasons behind the lack of consistency of cut off points and factor structures across studies we used Item Response Theory (IRT). Data were obtained from a population-based South London Stroke Register. 1132 stroke patients were assessed with the (HADS) 3 months after stroke (1995 - 2009). A two parameter (IRT) model was fitted using Mplus to examine items difficulty (an item property describing where the item function along the ability scale, for example more difficult items function among people with severe depression) and discrimination and to provide an underlying latent variable of psychological wellbeing. The most difficult item was "I can enjoy a good book radio or TV programme" with difficulty 1.85 (se: 0.15), the least difficult was "I feel I am slowed down". -0.19 (se: 0.06). Discrimination showed a less pronounced variance across items. Cut off points and scores that weight items equally are unlikely to reflect the correct underlying psychological status. A latent variable that takes into account response patterns and items properties would be more appropriate. The study extends the evidence on the superiority of Latent Class Models (LCM) for the assessment of health outcomes. P13.4 Application of a Latent-Class-Survival Model for data of a cardiological trial Lena Herich, Christine zu Eulenburg, Karl Wegscheider University Medical Center Hamburg-Eppendorf, Hamburg, Germany Structural equation models are a powerful tool for modelling associations between variables. Corresponding extensions are also possible for survival analysis problems [1], but are uncommonly used, especially in medical research [2,3]. The present research demonstrates the application of a latent class model based on Cox's proportional hazards assumption for risk prediction in patients post myocardial infarction. Numerous parameters describing different or similar aspects of Heart rate variability have been recorded at baseline. To handle multicollinearity, it seems plausible to assume a categorical latent variable to measure the different signs and symptoms observed. The effect of latent class membership, reflecting different patterns of disturbances, on time to event is then evaluated. The reliability of the model is investigated using bootstrapping techniques. Advantages and disadvantages compared to a standard Cox approach are discussed. Finally, the predictive accuracy of the two models will be compared. References [1] T. Asparouhov, K. Masyn, K., B. Muthen. Continuous time survival in latent variable models. Proceedings of the Joint Statistical Meeting in Seattle, August 2006. ASA section on Biometrics, 180-187. 103/156 [2] K. Larsen. Joint analysis of time-to-event and Multiple binary indicators of latent classes. Biometrics, 60:85-92. [3] T. Asparouhov, M. Boye, M. Hackshaw, A. Naegeli, B. Muthén. Applications of continuous-time survival in latent variable models for the analysis of oncology randomized clinical trial data using Mplus. Technical Report. P13.5 A Dynamic Prediction Model for Anticoagulant Therapy Peter Brønnum1, Søren Lundbye-Christensen2, Torben Bjerregaard Larsen0 1 Department of Cardiology, Aalborg Hospital, Aalborg, Denmark, 2Department of Cardiology, Aalborg AF Study Group, Aalborg Hospital, Aalborg, Denmark Patients with an increased risk of thrombosis require treatment with vitamin Kantagonists such as warfarin. Treatment with warfarin has been reported difficult mainly due to high inter- and intraindividual response to the drug. This poster reports the outcome of the development of a dynamic prediction model. It takes warfarin intake and International Normalized Ratio (INR) values as input, and uses an individual sensitivity parameter to model response to warfarin intake. The model is set on state-space form and uses Kalman filtering technique to optimize individual parameters. Retrospective test of the model proved robustness to choices of initial parameters, and feasible prediction results of both INR values and suggested warfarin dosage. Further studies to facilitate the impact of clinical outcome are currently under preparation. P14 Longitudinal data P14.1 A new criterion for choosing the best working correlation structure in GEE analisys M.C. Pardo1, R. Alonso2 1 Complutense University of Madrid, Madrid, Spain, 2Complutense University of Madrid, Madrid, Spain The method of generalized estimating equations (GEE) models the association between the repeated observations on a subject. An appropriate working correlation structure for the repeated measures of the outcome variable of a subject needs to be specified by this method since efficiency of analysis enhanced when intracluster correlation structure is accurately modelled. Some existing selection criteria choose the structure for which the covariance matrix estimator and the specified working covariance matrix are closest. We define a new criterion based on this idea for selecting a working correlation structure. We compare our criterion with some existing criteria to identify the true correlation structure via simulations with Poisson or binomial response, and exchangeable or AR(1) intracluster correlation structure. Our approach performs better when the true intracluster correlation structure is exchangeable as well as for an AR(1) structure since the proportion of selecting a true correlation structure was higher than that when other criteria were used. P14.2 Joint Hierarchical Generalized Linear Models Using H-likelihood Marek Molas1, Maengseok Noh3, Youngjo Lee4, Emmanuel Lesaffre1,2 1 Erasmus MC, Rotterdam, The Netherlands, 2Katholike Universiteit Leuven, Leuven, Belgium, 3Pukyong National University, Busan, Republic of Korea, 4 Seoul National University, Seoul, Republic of Korea H-likelihood offers an interesting framework to estimate models for longitudinal data (Lee and Nelder 1996, 2001). Solutions are found by maximizing the joint likelihood and the appropriate adjusted profile likelihoods. Obtained estimates are equivalent to REML estimates in the linear mixed model case, but here 104/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info they extend to the mean and scale mixtures of whole exponential family of distributions. The whole procedure relies on the neat numerical algorithm. Here we investigate the use of the h-likelihood methodology to estimate multivariate longitudinal models with response of exponential family. These are called Joint Hierarchical Generalized Linear Models (JHGLM). The numerical procedure uses the approach of h-likelihood method to estimate the fixed and random effects as well as residual and random-effect variance components. Random effects are allowed to be correlated within a model or between the models. Correlation between random components is estimated by using Newton - Raphson procedure. To estimate the correlation matrix the Cholesky decomposition is used and the score and hessian matrices are computed with respect to the parameters in this decomposition. We apply the method to a rheumatoid arthritis dataset, where two endpoints are measured: (1) a Health Assessment Questionnaire, which consists of binary data indicating remission of the disease or a good status, and (2) a Disease Activity Score (DAS) which evaluates the patient's disease status by a physician. The setting requires the estimation of a binomial-normal model, where correlations between latent trajectories of the two outcomes are of interest. years). ECG extractions were made from the Holter recordings and subsequently analyzed using the same process as the Standard 12-lead triplicate digital ECG. Seven ECG parameters were included in this study: PR interval (ms), Heart Rate (bpm), RR interval (ms), QRS interval (ms), QT interval (ms), QTcB interval (ms), and QTcF interval. The heterogeneous mixed model provides a simple approach to assessing the agreement in mean and variance of repeated measurements of an outcome variable between different methods. The ECG data analysis shows that although there is a good agreement between two methods in terms of mean values, Holter method produces consistently larger variance and lower intersubject correlation coefficients than the standard method in 7 ECG parameters. P14.3 The aim of the study was to investigate whether men who have sex with men (MSM) changed their sexual risk behaviour after notification of HIV-positive status. We used data from the Amsterdam Cohort Studies on HIV infection and AIDS. At every visit, participants were asked about their sexual risk behaviour over the preceding six months. We modeled trends in (un)protected anal intercourse from four years before the first HIV-positive test until four years after the first HIV-positive test. We fitted a random effects logistic regression and a marginal model using generalized estimating equations (GEE). A problem when fitting a random effects logistic regression model is that some individuals show consistent sexual behaviour. For example, 74% of the MSM reported to have practiced anal intercourse during every period before they became HIV infected. This violates the assumption that the random effects have a normal distribution on the logit scale. Therefore, we also fitted time trends using a latent class model, in which individuals could belong to three classes: (i): always "0", (ii): always "1" and (iii): switchers. Only if they belonged to class (iii), time trends were fitted using a random effects logistic regression. For parameter estimation, we used a Bayesian approach with MCMC sampling to summarize the posterior distributions. We compare results with the other two approaches. Also in the latent class model, estimated random effects for individuals in group (iii) did not have a normal distribution. However, marginal effects were in correspondence with the raw data. Causal inference with longitudinal outcomes and non-ignorable drop-out Maria Josefsson1, Xavier de Luna1, Lars Nyberg2 1 Umeå University, Umeå, Sweden, 2Umeå center for Functional Brain Imaging, Umeå, Sweden In many evaluation studies of a causal agent (treatment), analysts use observational data in which the treatment and control conditions are not randomly assigned to participants. By using propensity score matching the analysts can balance observed covariates between treatment and control groups and hence reduce potential bias in estimated causal effects. Incomplete data is common in longitudinal studies due, e.g., to participants’ death or withdrawal. Such drop-out is said to be non-ignorable when it depends on the participant’s underlying rate of change in the outcome. Not taking into account non-ignorable drop-out may yield biased estimates of causal effects. In this paper we propose a method for estimating the average causal effect of a treatment, at baseline, on the trajectories of some longitudinal outcome under the presence of non-ignorable drop-out. We illustrate this method with an analysis of the causal effect of living alone on memory performance based on data from a large longitudinal study conducted in Umeå, Sweden. P14.5 A random effects model fitted to dichotomous outcome data with latent classes Ronald Geskus1,2 1 Academic Medical center, Amsterdam, The Netherlands, 2Amsterdam Health Service, Amsterdam, The Netherlands P14.4 Assessment of Agreement between Digital 12-Lead ECG and continuous Holter ECG Recordings: A Heterogeneous Mixed Model Approach Duolao Wang1, Arne Ring2, Jorg Taubel3 P14.6 1 London School of Hygiene and Tropical Medicine, London, UK, 2University of Prognostic biomarkers across the patient journey: Systolic blood pressure Oxford, Oxford, UK, 3Richmond Pharmacology, London, UK before, during and after myocardial infarction Digital standard bed side 12-Lead ECG and continuous 12-Lead Holter ECG Eleni Rapsomaniki1, Harry Hemingway1, Liam Smeeth2, Adam Timmis3, Spiros Recordings are the two most commonly used ECG recording methods in Denaxas1, Julie George1, Anoop Shah1 thorough QT (TQT) studies and the assessment of agreement in ECG 1University College London, London, UK, 2London School of Health and measurements between two methods has important implications for design and Tropical Medicine, London, UK, 3Queen Mary University London, London, UK analysis of thorough QT studies. Background In this study, we propose a heterogeneous linear mixed model approach to evaluating the agreement in ECG parameters between Holter and standard Systolic blood pressure (SBP) is one of the most widely recorded biomarkers but, to our knowledge, previous studies have not evaluated the prognostic ECG recording method in terms of central locations and variations. impact of measurements taken before, during and after an acute myocardial The ECG data from a first into human trial are used for illustration of the infarction. proposed method. Standard 12-lead triplicate digital ECG were recorded using MAC1200 machines at specified time points and, in addition, continuous 12- Objective lead Holter ECG recordings were made in 34 healthy male subjects (25.7±7.5 To characterize the relationship between SBP and prognosis of acute ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info myocardial infarction, in relation to shape, longitudinal trends and variations in SBP. Methods We studied repeat SBP measurements from 24,300 patients who had suffered an MI from CALIBER, an electronic health records collaboration linking data from primary care, hospital admissions, a myocardial registry and causespecific mortality records. Study period was 2000-2010 with 4 years mean follow-up and the primary endpoint was subsequent (repeat) MI or fatal CHD. Associations were adjusted for usual confounders and medication use. Results SBP measured prior to MI has a positive linear association with subsequent events; in contrast, SBP measured after MI is associated inversely. Acute (admission) blood pressure, known to influence short term (e.g. 30 day) survival inversely, had no effect on the long term outcomes reported here. Variation in SBP measurements both before and after an MI was a predictor of future events. Particularly, standard deviation > 15mmHg among readings estimated from repeats within a 3-year period were associated with the worst prognosis. Conclusion The shape and direction of association of SBP with further coronary events after MI depend on the timing of SBP measurement. This may reflect impaired left ventricular function, resulting from myocardial infarction and persists after adjusting for blood pressure medications. P14.7 Discriminant analysis to predicting pre-eclampsia based on bivariate longitudinal biomarkers profiles Malihe Nasiri1, Soghrat Faghihzadeh1, Hamid Alavi Majd2, Farid Zayeri2, Noorosadat Kariman2, Nastaran Safavi2, Nasrin Boroomandnia2 1 Tarbiat Modares University, Tehran, Iran, 2Shahid Beheshti University of Medical, Tehran, Iran Discriminant analysis encompasses procedures for classifying observations into groups. In recent years, a number of developments have occurred in discriminant analysis procedures for longitudinal data. In different clinical studies, biomarkers are needed for early detection of a specific disease, taking into account any longitudinal information that becomes available. In longitudinal studies there are some situations that researcher want to analyze simultaneously two biomarkers for prediction of the outcome in discriminant analysis, because of their relations. Such example concerns pregnant women. The levels of hemoglobin and hematocrit can help to predict Pre-eclampsia. Levels that are abnormally high, may be signs that Pre-eclampsia is present. In a prospective cohort study, 650 pregnant women, who were referred to prenatal clinic of Milad Hospital were evaluated. Hemoglobin and hematocrit level in the first, second and third trimester of pregnancy was measured for each women. The subjects were followed up to record pre-eclampsia. Covariance pattern and linear mixed effect models are the methods that used in discriminant analysis for longitudinal data. The accuracy of the classification rule is described by the misclassification error rate (MER); The MER is estimated by the apparent error rate (APER).For the comparison of different models, we used APER and Aikake Information Criterion (AIC). The main objective of this article is to predict Pre-eclampsia by the longitudinal hemoglobin and hematocrit data and comparison of the models in univariate and bivariate longitudinal discriminant analysis by APER, AIC and ROC curve. The analysis was performed by SAS software. 105/156 Dorota Mrozek-Budzyn, Renata Majewska, Agnieszka Kieltyka, Malgorzata Augustyniak Jagiellonian University Medical College, Krakow, malopolska, Poland This study was designed to examine the relationship between neonatal exposure to thimerosal-containing vaccine (TCV) and child development. The cohort was recruited prenatally in Krakow. Child development was assessed using the Bayley Scales of Infant Development (BSID-II) measured with oneyear intervals over 3 years. Generalized Estimating Equation models adjusted for potential confounders were used to assess the association. Children exposed to TCV in neonatal period had significantly lower psychomotor BSID-II scores over the first three years of life than those not exposed (β=-3.13; 95%CI:-5.73;0.53). No association was found between TCV exposure and BSID-II mental tests scores. TCV exposure in neonatal period was associated with significantly lower psychomotor development scores during the first three years of life. P15 Meta-analyses P15.1 A Bayesian approach for multivariate meta-analysis with many outcomes Yinghui Wei1, Julian Higgins2 1 MRC Clinical Trials Unit, London, UK, 2MRC Biostatistics Unit, Cambridge, UK Multivariate meta-analytic models account for the dependency between effect size estimates and provide nature refinements over a univariate setting. However, the difficulties in estimation of parameters arise when there are missing outcome data. It becomes particularly challenging when there are many outcomes reported by a small number of studies. We propose a method based on the marginal distribution of the reported data and modelling of the heterogeneity parameters and correlation matrix separately. This facilitates incorporating informative empirically based prior distributions. We further consider reducing model complexity in terms of parameters in variance-covariance matrix. Several different covariance structures which can be common in practice are tested. We applied the methods to an example dataset from medical research practice. We observed precision increase in parameters estimates when the multivariate approach in used. Comparing DIC values from different models confirms the better fit of the multivariate model using the separation strategy. The analysis suggests benefits of reducing model complexity as it provide more precise parameter estimates. P15.2 Bayesian Meta-analysis of Diagnostic Tests Allowing for Imperfect Reference Standards Joris Menten1, Marleen Boelaert1, Emmanuel Lesaffre2,3 1 Institute of Tropical Medicine, Antwerp, Belgium, 2L-Biostat KULeuven, Leuven, Belgium, 3Dep. of Biostatistics, Erasmus University, Rotterdam, The Netherlands This work is motivated by a meta-analysis of the accuracy (Sensitivity Se and Specificity Sp) of rapid diagnostic tests (index test) for visceral leishmaniasis (VL). This meta-analysis is hampered by the lack of a perfect reference test for VL. This has two consequences: (1) in some studies Latent Class Analysis (LCA) is used instead of a reference standard; (2) in other studies a less than perfect reference standard may have been used. The statistical model used in the meta-analysis must combine results from studies analyzed with a reference standard with those from LCA while allowing for imperfect reference tests. P14.8 We extend the hierachical bivariate logistic normal model as follows. (1) The Neonatal exposure to thimerosal from vaccines and child development in the logit Se's and Sp's of index and reference tests for each study are modelled using bivariate normal distributions. (2) For studies using a reference standard, first 3 years of life - application of generalized estimation equasions the 2x2 contingency table of index vs. reference test result is modelled using a 106/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info multinomial distribution for the cell counts. (3) For studies estimating Se and Sp through LCA, the logits of the Se and Sp and the standard errors of these logits are derived from the estimates and CIs in the source publication. The model is estimated using Bayesian methods with non-informative priors for prevalences and Se and Sp of the index test. We classify studies according to the reference test and use informative priors for the diagnostic accuracy of the reference tests elicited from Leishmania experts. The performance of the model is studied through simulation and applied to the VL data. P15.3 Dichotomisation of continuous outcomes: A systematic review of metaanalyses using birthweight Mercy Ofuya1, Odile Sauzet2, Janet Peacock1 1 King's College London, London, UK, 2Universität Bielefeld, Bielefeld, Germany Background Power and precision are greater in meta-analyses than in individual analyses. However, dichotomisation of continuous outcomes poses a problem as studies can only be pooled if they have a common outcome. Meta-analyses may include analyses of the continuous and dichotomous form, with a different combination of studies for each. This may lead to loss of power and/or selection bias. Objectives (a)To perform a systematic review of meta-analyses for the outcome, birthweight, when analysed as continuous (in grams), or as dichotomous (usually low birthweight: birthweight <2500g), and assess the consequences of the duality of outcomes. (b)When dichotomised and continuous data were reported, to use the distributional method 1 to demonstrate how this could improve the precision of dichotomised outcomes. Methods Pubmed, Embase, Web of Science, and Cochrane Database of Systematic Reviews were searched for reviews published 2010-2011 with the outcome birthweight. Results Seventy-five meta-analyses were obtained. Of these, 61% (46) analyzed birthweight as continuous, and 73% (55) as dichotomous. Thirty-five percent (26) presented both dichotomous and continuous birthweight summaries. Among these, 6/26 reported results that were statistically significant for one outcome and not for the other. Conclusion Researchers commonly dichotomise continuous outcomes but do not always report results for means as well as proportions, leading to problems for metaanalyses. Presentation of both outcomes is needed. The distributional method would allow for more precise and unbiased estimates for dichotomised outcomes. 1. Peacock JL et al., Dichotomizing continuous data while retaining statistical power. Statistics in Medicine 2012. In press. P15.4 Metanalysis of high quality observational studies: a surrogate for clinical trials? Catherine Klersy, Marco Ferlini, Arturo Raisaro, Valeria Scotti, Anna Balduini, Luigia Scudelle, Carmine Tinelli, Annalisa De Silvestri IRCCS Fondazione Policlinico San Matteo, Pavia, Italy Often, despite evidence from large and/or high quality randomized clinical trials (RCT) is not available, there are numerous large, high quality cohort studies presenting adjusted risk ratio (RR). We considered studies evaluating efficacy of coronary intravascular ultrasound (IVUS) guidance in drug eluting stent positioning and compared evidence derived from RCT and longitudinal observational studies. We performed an exhaustive literature search for full-text articles in peerreviewed journals (2003-2011), excluding uncontrolled studies. We metanalyzed major adverse cardiac events (MACE: death, acute myocardial infarction and/or revascularization). Pooled fixed or random effect RR and 95% confidence intervals (95%CI) were computed Twenty-six full texts out of 217 abstract were retrieved; 17 of them were excluded, (abstract, review, metanalysis, MACE missing, no pertinence). Of the 9 included articles,1 was a RCT and 8 were observational (5 with adjusted estimates). A total of 17541 patients were enrolled. Median follow-up duration was 18 months (25th-75th percentiles 12-24). The RR for the RCT was 0.92 (0.39-2.19, N=210 pts); for the ‘adjusted' cohort studies it was 0.79 (0.69-0.91, N=15405 pts) and for the ‘unadjusted cohort" it was 0.89 (0.70-1.13, N=2286 pts). The small RCT does not provide sound evidence, as shown by wide 95%CIs, while the large well-conducted cohort studies with adjusted estimates indicate a protective effect of IVUS towards MACE, with high level of confidence. Being the IVUS patients more critical, its effect would be diluted in unadjusted studies. Although large RCTs are needed to confirm IVUS role, good quality cohort studies might better reflect real life. P15.5 Estimation of between-trial variance in sequential meta-analyses Putri Novianti, Kit Roes, Ingeborg van der Tweel Biostatistics and Research Support, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands Various estimators of the variance between treatment effects from randomized clinical trials in a meta-analysis are known to be divergent or even conflicting. In a sequential meta-analysis (SMA), their properties are even more important, as they influence the point in time at which definite conclusions are drawn. To study their properties in an SMA, we conducted an extensive simulation study and applied the estimators in real life examples with dichotomous and continuous outcome data. Bias and variance of the estimators were used as primary evaluation criteria, as well as the number of patients from the accumulating trials needed to arrive at stable estimates. Results of simulation studies showed that the well-known DerSimonian-Laird estimator behave differently for dichotomous as compare to continuous outcomes. The DerSimonian-Laird, the REML and the Paule-Mandel estimator all perform well in an SMA with continuous outcome data. For an SMA with dichotomous outcome data the Paule-Mandel estimator is to be preferred. P15.6 Multivariate meta-analysis of surrogate endpoints in health technology assessment: a Bayesian approach Sylwia Bujkiewicz1, Alex J. Sutton1, Nicola J. Cooper 1, John R. Thompson1, Mark J. Harrison2, Deborah P. M. Symmons2, Keith R. Abrams1 1 University of Leicester, Leicester, UK, 2University of Manchester, Manchester, UK Surrogate endpoints play an increasingly important role in health technology assessment, in which clinical effectiveness outcomes are used to derive quality of life estimates (e.g. EQ5D), as part of the economic evaluation of the new medical technologies. These effectiveness measures are usually estimated by meta-analysing outcomes from randomised controlled trials. However when few clinical trials report the clinical outcomes appropriate for economic models, the surrogates can be considered through multivariate meta-analysis of studies that are mixture of those reporting the primary outcome of interest, a surrogate endpoint or both (preventing valuable data from clinical trials from being discarded). In a Bayesian framework, multivariate random effects meta- ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info analysis has the advantage of borrowing strength between studies and between the outcomes. This can lead to reduced uncertainty around the effectiveness estimate and, in consequence, of the estimates of the costeffectiveness. This framework will be presented based on an example from rheumatoid arthritis, in which EQ5D is derived from change in HAQ score (a self-reported measure of physical function). Multivariate evidence synthesis is used to take into account data from studies reporting HAQ as well as studies reporting other measures of response such as ACR, DAS28 or EULAR with the aim of decreasing uncertainty around HAQ. Multivariate meta-analysis in a product normal formulation (PNF) (of conditionally independent univariate normal distributions) will be discussed with the prior for within-study correlation obtained from individual patients' data. We will discuss sensitivity to prior distribution for between-study correlation and implied priors for the parameters of the PNF model. 107/156 for placebo in cUTI. The primary efficacy endpoint was microbiological eradication rate at the test of cure visit in the microbiological intent-to-treat population. Adjustments were made to account for inconsistency in time of primary endpoint assessment among some studies. To account for inter-study variability, a weighted non-iterative random-effects model was used to obtain an estimate of the eradication rate using a meta-analysis package in R (metafor). The estimated eradication rates and 95% confidence intervals (CI) were 32.2% (26.7%-37.8%) for placebo, 81% (77.7%-84.2%) for Doripenem, 79% (75.9%-82.2%) for Levofloxacin, and 80.5% (71.9%-89.1%) for Imipenemcilastatin. The treatment effect was estimated as the difference between the lower bound of the CI for each comparator and the upper bound of the CI for placebo. The treatment effect estimates were 39.9% for Doripenem, 38.1% for Levofloxacin, 34.1% for Imipenem-cilastatin, and 40.1% for overall. These estimates will facilitate the design of future cUTI trials and help inform appropriate NI margins. P15.9 An age-adjusted metric for risk discrimination, with application to age-specific cardiovascular disease prediction Eleni Rapsomaniki1, Ian White2, Simon Thompson3, Angela Wood3, John Danesh3 Very large sample sizes are required for estimating effects which are known to 1University College London, London, UK, 2MRC Biostatistics Unit, Cambridge, be small, and for addressing intricate or complex statistical questions. Such UK, 3Strangeways Research Laboratory, Cambridge, UK sample sizes are often only achievable by pooling data from multiple studies; effects of interest can then be investigated through an individual-level meta- Discrimination statistics such as the C-index describe the ability of a survival analysis (ILMA) on the pooled data, or via a study-level meta-analysis (SLMA). model to assign higher risks to individuals who experience earlier events. Their value depends on the sample distribution of prognostic variables, but this - in Ethico-legal constraints that govern the agreements and consents for individual particular the age distribution - is partly controlled by study design. This makes studies frequently prohibit pooling experimental data. By conducting a SLMA in discrimination statistics difficult to compare across studies that include different place of an ILMA, the data are not pooled and only non-disclosing summary age-groups and may obscure the predictive power of additional risk factors. statistics are shared between studies. To overcome this limitation we propose two new discrimination measures: a It is possible in certain cases to conduct an ILMA without pooling data from stratified C-index computed within age-bands, and an adjusted C-index based multiple studies, and without disclosing sensitive information: the original on the age-band-specific measures. We present results based on individual-level analysis, in some cases, can be pieced back together using cardiovascular disease (CVD) risk prediction and associated risk factors, using only non-disclosing summary statistics from each study. When the data are baseline measurements from 350,000 participants, aged 40 to 89, from 90 horizontally partitioned between studies, that is, data are collected on the same cohort studies from the Emerging Risk Factors Collaboration. We show that variables in each study and a particular study participant can be found in one between-study heterogeneity in the adjusted C-index is almost 50% less than study only, it is possible to build, for example, an individual level generalised in the ordinary C-index in a model adjusted for age and sex, and 13% less in a linear model, or conduct a generalized estimating equations analysis, without multiply adjusted model. Further, age modifies the prognostic significance of pooling the data. CVD risk factors, most of which were found to be significantly more We introduce DataSHIELD (Data Aggregation Through Anonymous Summary- discriminative at younger ages. In fact, in those aged 80-89 we observed no statistics from Harmonised Individual levEL Databases) as a tool to coordinate improvement in discrimination when risk factors were added to a model such an analysis, and explain why this approach yields identical results to an adjusted for age and sex alone. ILMA for a range of statistical analyses. IT requirements will also be discussed, We conclude that meta-analyses of concordance statistics and their along with the ethical and legal challenges which must be addressed. incremental values should pay attention to study-specific distributions of age P15.8 and other design variables, and that stratified and adjusted C-indices avoid Meta-analysis to estimate the treatment effect of Doripenem, Levofloxacin, and spurious heterogeneity. Imipenem-cilastin in complicated urinary tract infections Krishan Singh, Gang Li, Linda Mundy, Fanny Mitrani-Gold, Jeffrey P15.10 Wetherington, Milena Kurtinecz Meta-analysis of paired-comparison studies of diagnostics test data: A GlaxoSmithKline Pharmaceuticals, Collegeville, Pennsylvania, USA Bayesian modelling approach P15.7 Individual-level statistical analysis without pooling the data Elinor Jones, Nuala Sheehan, Paul Burton University of Leicester, Leicester, UK Active-control, non-inferiority (NI) study designs are commonly used to establish the effectiveness of a new antimicrobial drug for treatment of serious infections such as complicated urinary tract infection (cUTI). A meta-analysis was conducted to estimate the treatment effect of three potential comparators: Doripenem, Levofloxacin, and Imipenem-cilastatin, for the design of an NI trial. A systematic literature review used a priori criteria for selection of randomized clinical trials, including assessment of publication quality using the validated 3item Jadad metric. No historical placebo-controlled cUTI trials were identified. Three placebo-controlled trials in uncomplicated UTI were identified as a proxy Pablo Verde University of Duesseldorf, Duesseldorf, NRW, Germany Diagnostic paired-comparison studies arise when two diagnostic tests are applied to the same group of patients. In such studies accuracy characteristics (e.g. sensitivities and specificities) are correlated between tests. The main problem in meta-analysis of this type of data is the lack of published information to account for intra-study correlation and to directly analyze between tests agreement. In this work we borrow ideas of ecological inference 108/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info for 2x2 tables (Wakefield, 2004) to model indirectly the intra-study correlation of accuracy characteristics and to infer on tests agreement. Variability between studies is modeled by extending a Bayesian hierarchical model for metaanalysis of diagnostic test (Verde, 2010). Statistical methods are illustrated with two systematic reviews. The first one investigates the diagnostic accuracy of automatic decision tools (e.g. neural networks) compared with unaided doctors in patients with acute abdominal pain (Liu, et al. 2006). The second one compares the diagnostic accuracy positron emission tomography with computer tomography in the detection of lung cancer (Birim, et al. 2005). A simulation experiment is presented to assess the influence of ignoring intrastudy correlation in this meta-analytic problem. Statistical computations are implemented in public domain statistical software (JAGS and R). Keywords: multivariate meta-analysis, diagnostic test, ecological inference, MCMC suggest that misspecification of lower cluster levels has worse impact on estimation than misspecification of higher levels. The variance of unobserved heterogeneity and the location of the probability's outcome are important determinants of the size of bias and largest bias occured where response was rare. The impact on structural parameters is less serious than that on random parameters. Three levels of unobserved heterogeneity with mean zero and a variance between 0.01-1.0, were examined, using a single or two binary explanatory variables that were time varying or time invariant. The findings may appeal to researchers aware or in doubt about the availability of clustering at one or more levels but intending to use a simple approach. {1}Ayis, S. Testing Inference from Logistic Regression Models in Data with Unobserved Heterogeneity at Cluster Levels. Communications in Statistics, 2009 Vol. 38 (6) 1202-1211 P16.3 P16 Model selection A map for the jungle of choices in Mixed Models for Repeated Measures Sophie Swinkels, John Hendrickx P16.1 Danone Research, Wageningen, The Netherlands Inverse problem within a regression framework 1,2 Abdel Douiri Mixed models for repeated measures offer different options for modeling the 1 King’s College London, London, UK, 2NIHR c-BRC, Guy’s and St. Thomas’ mean, modeling the covariance, modeling time, handling baseline responses and for defining the intervention effect. For modeling the mean options include NHS Foundation Trust, London, UK analyzing response profiles and analyzing parametric curves. Covariance can Regression modelling is a powerful statistical tool often used in clinical trials be modeled using various covariance pattern models or random effects and epidemiological studies. The regression problem could be formulated as covariance structures. Time can be modeled as fixed occasions or as a an inverse problem that measures the discrepancy between the target outcome continuous variable. Baseline can be included as covariate or in the response and the data produced by representation of the modelled predictors. This vector together with the post baseline values. The intervention effect can be approach could simultaneously perform variable selection and coefficient defined at a specific time point or considering the whole intervention period. estimation. We focus particularly on linear regression problem, Y ∼ N (Xβ*, Theoretically, each choice can be combined with all the options for the other σIn), where β* ∈ Rp is the parameter of interest and its components are the choices resulting in a huge amount of possibilities for the total model. One can regression coefficients. The inverse problem finds an estimate for the easily get lost in this wilderness of possibilities. parameter β*, which is mapped by the linear operator (L: β→Xβ) to the observed outcome data Y= Xβ+ε. This problem could be conveyed by finding a During this presentation, a scheme will be introduced that summarizes all these solution in the affine subspace L-1({Y}). However, in the presence of options and provides guidance in selecting the most suitable choice. Contrasts multicollinearity or/and high dimensional data, the solution may not be unique, will be used to compare common elements of different models and to show so the introduction of a prior information to reduce the subset L -1({Y}) and they may not be as different as they appear on first sight. The impact of the regularize the inverse problem is needed. Inspired by Huber's robust statistics different model options will be illustrated using data from a multi-centre multiframework, we propose a new extension to l1-penalized regression problem: an country, randomized, controlled, double-blind, parallel-group trial, designed to adaptive Hubert regularizer. A simple method for selection of the regularization assess the effect of a medical food on memory performance in patients with parameter that minimizes the l1-penalized likelihood will be given. We compare mild AD. results of adaptive Hubert and two other l 1-penalized regression regularization methods, Adaptive lasso and elastic net, on both simulated data and real data P16.4 from the South London Stroke Register. The proposed approach can be Variable selection for prediction models based on multicenter data extended to general linear regression models. Laure Wynants1,2, Dirk Timmerman3, Sabine Van Huffel1,2, Ben Van Calster1 1 Department of Electrical Engineering (ESAT-SCD), KU Leuven, Leuven, P16.2 Belgium, 2IBBT Future Health Department, KU Leuven, Leuven, Belgium, Quantifying Bias due to Unobserved Heterogeneity at Individual and Cluster 3Department of Development and Regeneration, KU Leuven, Leuven, Belgium Levels when Using Binary Response Regression Models: A Simulation study Ignoring the clustered nature of multicenter data biases multivariable model Salma Ayis, Bola Coker building. Therefore, we investigated how this affected model coefficients and King's College London, London, UK variable selection for the development of a prediction model for ovarian tumor Unobserved factors that affect outcomes of interest are common in medical diagnosis. and behavioural data. For example differential response to treatments might be To predict malignancy of ovarian masses, two models were developed on data attributed to unknown characteristics (unobserved heterogeneity) and these for 19 predictor variables from 3510 patients recruited at 21 centers. Method 1 may seriously impact on inference from binary regression models. Simulation ignored the clustered nature of the data whereas method 2 included a random was used to study factors that influence the bias of the estimated parameters, intercept for center. We first fitted the full models for both methods to compare including the variance of unobserved heterogeneity, the clustering level that model coefficients. Then we performed backward variable elimination (with was missing, and models used for estimation. This investigation extends an p=0.01 as selection criterion) using bootstrapping to compare selection earlier work{1} using the standard and mixed effect logistic models to adjust for frequencies. Finally, we demonstrated the combination of random intercepts unobserved heterogeneity at cluster levels 1, 2 and 3. Situations where the logistic regression with multivariable model building using the method of clustering level was correctly specified or missing were examined. Findings multivariable fractional polynomials (MFP). ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info For the full models, model coefficients differed by at least 10% for seven variables, with a mean difference of 12% and a maximum of 74%. Variable selection frequencies also depended on whether clustering by center was accounted for. The selection percentage differed by more than 10 percentage points for five variables, with the highest difference being 64 percentage points. Finally, the MFP method selected fewer variables when a random intercept was used compared to when clustering by center was ignored. Inter-center differences in predictor effects resulted in more conservative results when using a random intercept, with higher p-values and lower selection frequencies. Whereas multicenter data are beneficial for increasing model generalizability, the clustering by center must be accounted for when developing prediction models. P16.5 Generalized Additive Models and Computerized Breast Cancer Detection: Clinical Application Javier Roca-Pardiñas1, María J. Lado1, Pablo G. Tahoces2, Carmen CadarsoSuárez2 1 Universitity of Vigo, Vigo, Spain, 2University of Santiago, Santiago de Compostela, Spain 109/156 under the drug concentration-time curves (AUCs) for two oral-administered drugs. To do so, we construct a nonlinear mixed effect model for the drug concentration-time profiles in the 2x2 cross over design. In the statistical model, one-compartment PK models are employed to describe the drug concentration-time curves which incorporate both the between-subject and within-subject variations. Moreover, a multivariate generalized gamma (MGG) distribution is introduced for the drug concentrations repeatedly measured from the same volunteer at different periods in each of the two sequences. We then propose a test for the bioequivalence between the two drugs based on the discrepancy of the two AUCs estimated from the proposed model fitted into the observed drug concentration-time profiles. The results of a simulation study investigation the level and power of the model-based test relative to the conventional non-compartmental test under different configurations of MGG distributions, sample sizes and measured time points are also presented. Finally, a real data set is illustrated based on the proposed model and test. P17.2 Sample size and power considerations for estimating subject-treatment interactions in parallel-group designs using covariates Ruediger Paul Laubender Institute of Medical Informatics, Biometry, and Epidemiology (IBE), Munich, Generalized Additive Models (GAMs) are mathematical models that can be Germany used to predict the mean of a response variable, depending on the values of other explicative covariates. They allow for the introduction of categorical When the aim of a clinical trial is to estimate treatment-subject interactions variables, called factors, which can be essential in diverse classification (e.g. for identifying responders), it is usually necessary to rely on repeated crossover designs. However, such designs are not well suited for non-chronic problems. diseases (e.g. cancer) and treatments which irreversibly alter the patients for a In this work, we have studied one specific classification problem, derived from long time (e.g. vaccines) so that only parallel-group based designs can be the development of a Computer-Aided Diagnosis (CAD) system, dedicated to used. Laubender (2011) recently proposed a maximum likelihood based model the early detection of one of the primary signs of breast cancer: clustered for estimating subject treatment interactions for normally distributed outcomes microcalcifications. based on the parallel-group design by redefining a predictive covariate as an Nowadays, breast cancer is one of the most usual cancers. Clustered indicator for the individual treatment effect and by relying on a multivariate microcalcifications are relevant radiologic signs of irregular shape, varying size, normal distribution. The planning of parallel-group based trials using this model and located in an inhomogeneous background of parenchymal tissues, which depends on three conditions whether subject-treatment interactions are appear in 30%-50% of breast cancers. present at all. Therefore, methods for sample size and power calculations are To help radiologists to early diagnose breast cancer, CAD schemes have been derived which are based on three partially correlated test statistics. Further, developed during the last decades. These systems produce, as a result, simulation studies were performed to evaluate the performance of these suspicious areas that should be analyzed to determine if they correspond sample size and power calculations for different scenarios. Finally, the model of Laubender (2011) makes some crucial assumptions about how nature is either to a lesion or a false detection. In this work, we have developed an approach, based on GAMs and ROC supposed to generate data. These assumptions are presented and statistical (Receiver Operating Characteristic) analysis, for reducing the number of false methods are offered for checking these assumptions using data from clinical positives in a CAD system for detecting microcalcifications. A factor was also trials. included, considering as the categorical covariate the breast tissue. Different Reference: sets of properties of the detected clusters were selected and analyzed Laubender RP. A statistical approach for identifying responders in vaccine employing GAMs. The software was developed employing R, and our results studies. Medical Corps International Forum 2011; 4./4-2011. yielded a high performance of the system (high sensitivity and reduced number of false detections), when the appropriate group of covariates was selected. P17.3 P17 Modelling in drug and device development P17.1 Model-based bioequivalence test in a 2x2 cross over design Yuh-Ing Chen, Chi-Shen Huang National Central University, Jhongli, Taiwan In a pharmacokinetic (PK) study, to investigate if the test and reference drugs under consideration are bioequivalent, two sequences of volunteers are usually assigned to take different drugs in two periods under a 2x2 cross over design. The drug concentrations in blood or plasma are then repeatedly measured from the same volunteer at certain time points after the drug administration which is referred to as the drug concentration-time profile or curve. In this paper, we are concerned with testing for the equivalence between the areas Development of a tool to elicit experts' beliefs for medical device evaluation Leslie Pibouleau, Sylvie Chevret INSERM UMR S717; Hôpital Saint Louis; Université Paris Diderot - Paris 7, Paris, France RATIONALE: Bayesian methods appear particularly interesting in implantable medical device (IMD) evaluation because high quality clinical information on IMDs is rare whereas substantial prior information often exists. In this context of uncertainty due to the lack of sound data, the use of experts' beliefs to inform prior distributions is a key component. OBJECTIVE: To develop a computer-based tool available on-line for expert opinions elicitation about the distribution of a parameter involving a Bernoulli process. METHODS: Information to be elicited was the distribution of success rate of an intracranial stent. It was encoded using the fixed interval method. The elicitation 110/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info questionnaire consisted of five parts: 1) an identification form, 2) a question on the predictive factors of success, 3) a training exercise, 4) the elicitation question itself and 5) a feedback form on the ease of use of the questionnaire. All corresponding authors of clinical articles on intracranial aneurysms published in the last six years (n=356) were invited by e-mail to participate to the survey. RESULTS: Twenty experts (5.6%) completed the survey. Individual prior distribution was elicited for each participant. Feasibility was judged in view of the experts' feedback and time to completion. Validity and reliability were assessed using data on comprehensiveness, internal coherence and testretest reliability. CONCLUSION: This method of belief of elicitation is feasible, valid and reliable. It should be useful to promote the use of Bayesian methods in IMD evaluation. P18 Modelling infectious disease P18.1 Model Based Estimates of Long-Term Persistence of Induced HPV Antibodies: A Flexible Subject-Specific Approach Mehreteab Aregay1, Ziv Shkedy2, Geert Molenberghs2, Marie-Pierre David3, Fabian Tibaldi3 1 I-BioStat, Katholieke Universiteit, Leuven, Belgium, 2I-BioStat, Universiteit Hasselt, Hasselt, Belgium, 3GlaxoSmithKline Biologicals, Rixensart, Belgium In infectious diseases, it is important to predict the long-term persistence of vaccine-induced antibodies and to estimate the time points where the individual titers are below the threshold value for protection. This poster focuses on HPV16/18, and uses a so-called fractional-polynomial model to this effect, derived in a data-driven fashion. Initially, model selection was done from among the second- and first-order fractional polynomials on the one hand, and the linear mixed model on the other. According to a functional selection procedure, the first-order fractional polynomial was selected. Apart from the fractional polynomial model, we also applied a power law model, which is a special case of the fractional polynomial model. Both models were compared using Akaike's information criterion. Over the observation period, the fractional polynomials fitted the data better than the power-law model; this, of course, does not imply that it fits best over the long run and hence caution ought to be used when prediction is of interest. Therefore, we point out that the persistence of the antiHPV responses induced by these vaccines can only be ascertained empirically by long-term follow-up analysis. P18.2 Statistical models for biosurveillance: an empirical investigation Doyo Enki1, Angela Noufaily1, Paddy Farrington1, Paul Garthwaite1, Nick Andrews2, André Charlett2 1 The Open University, Milton Keynes, UK, 2Health Protection Agency, London, UK We consider the problem of choosing appropriate statistical models for large multiple surveillance systems to detect outbreaks of infectious disease. In such systems, simple, robust algorithms are required, as detailed model selection and model checking is not practical. One common approach is to use quasilikelihood methods, with detection thresholds based on normal approximations. However, such thresholds may be inappropriate for uncommon infections, as they are found to inflate the false positive detection rate. We use twenty years' data from a large laboratory surveillance database used for outbreak detection in England and Wales, involving over 3300 distinct organism types, to study empirically which parametric families of distributions are likely to be appropriate. In particular we focus on the negative binomial distribution with mean μ and variance of the form Φμ, with Φ > 1. We investigate the mean-variance and mean-skewness relationships for over 1000 organisms with evidence of extra-Poisson dispersion. We find strong evidence of systematic patterns, which suggests that parametric modelling of very diverse organisms within a single parametric family is feasible. However, there is also evidence that the negative binomial allows for insufficient variability and skewness, though the discrepancy is generally small. We discuss alternative approaches in the light of these results. P18.3 Epidemiological modelling of risk factors of human papilloma virus in women with positive cytology in the county of Csongrád Epidemiological modelling of risk factors of human papilloma virus in women with positive cytology in the county of Csongrád Tibor Nyári, Krisztina Boda Department of Medical Physics and Informatics, University of Szege, Szeged, Hungary Cervical carcinoma, one of the most frequent malignancy in women worldwide, is a major human cancer the viral etiology of which can be considered. This study was carried out to determine the prevalence and risk factors of genital HPV infection in women diagnosed with non-negative cytology in southeastern Hungary. Cervical samples were collected for cytology and HPV testing from women seen at gynaecological outpatient clinics and diagnosed with non-negative cytology. Logistic regression analysis was applied to obtain an overview of the risk of HPV infection. A total of 72 women diagnosed with positive cytology were examined for the prevalence of HPV. The observed overall average HPV infection rate was found to be 61% (44/72). High-risk HPV positivity was detected in 38 samples (86%). There were 5 condyloma cases in the HPV-infected group (11%) and 1 condyloma case (3%) in the HPV-negative group. The difference between the mean ages of the HPV-infected patients (n=44; mean age: 30.2 years, SD: 9.3 years) and the non-infected women (n=28; mean age: 31.2 SD: 7.5 years) was statistically non-significant. The smoking habit proved to be the only risk factor in the logistic regression analysis that related significantly to the exposure to HPV infection (p=0.02). Thus, prevention strategies should focus on the regular clinical cytological screening of HPV-infected patients and on the reduction of smoking. This study was supported by TÁMOP 4.2.1./B-09/KONV-2010-005 project. P18.4 An alternative to incorporate the number of persons tested when looking for time trends in STDs Achilleas Tsoumanis, Inga Velicko, Sharon Kuhlmann-Berenzon Swedish Institute for Communicable Disease Control (SMI), Stockholm, Sweden When studying time trends of sexually transmitted diseases (STD) based on surveillance data most often the incidence (cases per population) is modeled with time as the only covariate. It has been observed, however, that the number of cases is highly correlated to the number of persons tested, and thus a trend in incidence might be strongly affected by changes in the population's testing behavior. An alternative has been to include the number of persons tested as a covariate in the model. These models, while they seem to describe incidence quite accurately, they often lead to misinterpretations regarding the trend when their results are presented. To circumvent the high correlation between number of cases and persons tested we propose to model the number of cases per tested, either as the actual ratio (case ratio or CR) as a Gamma-GLM, or by modeling the number of cases, using the number of tested as offset in a negative binomial GLM. If the number of cases and persons tested change with similar rates, their ratio will remain constant and thus no trend is found. We compared the two ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info suggested models to a negative binomial GLM of incidence. Data describe annual number of Chlamydia cases in Sweden obtained from the surveillance system for 1998 to 2004. The three models gave similar results in terms of predictive values and time trend. The model with CR gives a more realistic image of the trend in STDs since it reflects the number of tested simultaneously. P19 Observational studies 111/156 matrix, we also pick up the concept of 'unconditional power' (Gluck and Muller, 2003), which is obtained by integrating over the distribution of the conditional power. We evaluate our methods in a simulation study that covers some specific cases with either normally or binomial distributed risk factors. Demidenko, E. (2007) Sample size determination for logistic regression revisited. Statistics in Medicine, 26, 3385-3397. Glueck, D.H. and Muller, K.E. (2003) Adjusting power for a baseline covariate in linear models. Statistics in Medicine, 22, 2535-2551. Senn, S. and Bretz, F. (2007) Power and sample size when multiple endpoints are considered. Pharmaceutical Statistics, 6, 161-170. P19.1 Attributable fractions of one year mortality after diagnosis of lung cancer Geir Egil Eide1, Knut Skaug2, Amund Gulsvik3 P19.3 1 Centre for Clinical Research, Haukeland University Hospital, Bergen, Norway, Analysis of clustered binary data with extreme proportions 2 Helse Fonna, Haugesund, Norway, 3Lung Depa, Haukeland University Elizabeth McKinnon Hospital, Bergen, Norway Murdoch University, Perth, WA, Australia Background: Lung cancer has high short-term mortality rates determined by severity of disease at diagnosis. Early detection is crucial for survival. Few Clustered data arise frequently in observational studies, for example when studies have estimated the potential gain in short-term mortality from possibly collected from patients at intermittent clinic visits. Here we consider the analysis of clustered binary data, motivated by a study of predictors of aviremia diagnosis at earlier stages. among HIV positive patients successfully treated with suppressive antiretroviral Aim: To quantify the potential reduction in one-year mortality from early therapy. Whilst the analysis of clustered binary data has received diagnosis. considerable attention over the years, there has been less focus on covariate Method: This is part of a long-term follow-up study of all 271 incident lung assessment or estimation difficulties when the underlying cluster-specific cancer patients in the Haugaland area, Norway, from 1st January 1990 to 31st probabilities being estimated may be close to zero or one. A common issue December 1996. By estimating attributable fractions (AF) based on a logistic arising in logistic regression analyses of non-clustered data with extreme regression analysis of first year mortality on demographic variables, anatomical proportions is that of potential separation, when the likelihood converges stage, functional performance status and treatment, the potential effect of early without concomitant parameter convergence. We therefore investigate similar detection of lung cancer is quantified. The results are illustrated by a direct issues in the application of standard analytic methods for clustered data based acyclic graph and an ordered stepwise strategies diagram. Results: The first on generalized estimating equations or mixed effect models, and compare their year 192 patients (71%) died. Age (<65, 65-75, >75), stage (I+II, III, IV), performance to that of alternative approaches including a simple method which performance status (0+1, 2, 3+4) and treatment (surgery, chemo/radio-therapy, utilizes weighted estimating equations. To address the estimation and supportive only) significantly influenced survival. Adjusted AF %s and convergence problems that arise due to the extreme parameter estimates, we confidence intervals (CI) were estimated for age 65+: 11.0, 95% CI: (1.8, 19.4); also investigate the utility of a Firth-like penalized likelihood. stage III+IV: 30.5, 95% CI: (15.4, 42.9); performance status 3+4: 3.1, 95% CI: (0.8, 5.4); and supportive treatment: 12.2, 95% CI: (1.2, 22.0). The combined AF was 79.1, 95% CI: (59.1, 89.3). The combined AF due to the three clinical P19.4 Multistage dynamic sampling design for observational studies variables adjusted for age was 64.2 (47.0, 75.9). Conclusions: This study confirms that there is a large potential gain in one-year Michel Hof, Anita Ravelli, Aeilko Zwinderman survival and that the method of attributable fractions is useful for discussing Academic Medical Center, Amsterdam, The Netherlands risk-reduction strategies. In population cohort studies the main objective of the sampling design is to have a sample that is representative for the population. With a representative P19.2 sample, the prevalence of certain diseases or features of the population can be Power Approximation for Logistic Regression Models with Multiple Risk Factors accurately estimated. in Observational Studies A complication that arises in the recruitment of a representative sample is heterogeneity in participation willingness. For instance, it is known that males Klaus Jung, Tim Friede Department of Medical Statistics, University Medical Center Göttingen, and females have a substantial difference in participation willingness. To deal with this heterogeneity, multistage sampling methods have been proposed. In Göttingen, Germany each stage, a small sample of individuals is invited to the study with a sampling Logistic regression is one of the most frequently used tools for the analysis of method that can deal with different inclusion probabilities. data from observational studies. In contrast, sample size methods for planning In this study we consider two sampling methods for each stage: the cube such studies are rather rare and focus mostly on simplistic situations. For method developed by Deville and Tillé and a new dynamic sampling method. instance, Demindenko (2007) presents sample size formulas for models with This dynamic sampling method sequentially invites individuals that minimize one continuous risk factor that is accompanied by several confounding the difference between the joint distributions of the relevant variables, which covariates. In this presentation we focus on studies in which several risk are used to check whether the sample is representative for the population. In factors are of equal interest. Based on an approximation of the joint distribution addition, these -to be invited- individuals are weighted with their estimated of the regression coefficients for all risk factors we propose a new approach for probability to participate. power calculations. More specifically, we regard the concept of 'disjunctive power', i.e. the probability that at least one of the risk factors is significantly The performance of the dynamic sampling designs was evaluated with associated with the response (Senn and Bretz, 2007). Since the power results simulations. We considered scenarios of cohort studies with and without of our approach are initially conditioned by the randomness of some design heterogeneous response rates, in which an estimation of the prevalence of a 112/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info particular disease was desired. In both scenarios, the proposed multistage P19.7 dynamic sampling design was considerably more accurate than the multistage A comparison of different multilevel models to analyse the effect of maternal cube method. obesity on pregnancy induced hypertension Edwin Amalraj Raja, Amanda J Lee, Fiona Denison P19.5 University of Aberdeen, Scotland, UK Impact of measurement error and unmeasured confounding: a simulation study Background: Maternal bodymassindex (BMI) is an important determinant of based on the example of ascorbic acid intake and mortality pregnancy outcome with underweight and obesity being associated with an 1 2 2 1 1 2 RHH Groenwold , K Tilling , DA Lawlor , KGM Moons , AW Hoes , JAC Sterne increased risk of perinatal complications. The aim of this study was to 1 Julius Center for Health Sciences and Primary Care, University Medical compare the use of three multilevel models (Population averaged (PA), random Center Utrecht, Utrecht, The Netherlands, 2School of Social and Community effect (RE) and Fixed effect (FE)) to quantify the effect of maternal BMI on the Medicine, University of Bristol, Bristol, UK risk of pregnancy induced hypertension (PIH). Results from observational studies are potentially biased due to unmeasured or Methods: Routinely collected data from all women who delivered in Scottish misclassified confounders. For example, the substantial reduction in mortality maternity units between 2003 and Dec 2009 were included. Maternal BMI at by ascorbic acid (OR 0.48, 95%CI 0.33-0.70) found in a nonrandomized study ≤16 weeks gestation was categorised as underweight, normal, overweight, was suggested to be biased for these reasons. We assessed to what extent obese and very severely obese. Models were adjusted for potential observed associations, such as between ascorbic acid and mortality, can be confounders. explained by unmeasured or misclassified confounders. Results: The final dataset consisted of 124 280 deliveries nested within 109 We simulated datasets of 100,000 subjects using parameters from the British 592 women. The PA model odds ratio (OR) is interpreted as the average odds Women's Heart and Health Study, including 28 confounders (6 continuous, 22 of PIH among obese women compared with normal BMI women (OR 2.35; dichotomous). Unmeasured confounding was evaluated by including only 7 95% CI 2.18, 2.54). The RE model estimates the odds of PIH (OR 2.98; 95% strong confounders in the adjustment model. Misclassification of confounders CI 2.65, 3.36) for obese women among all women with the same risk. The FE was assessed by adding a random value (drawn from a normal distribution model shows the odds of PIH (OR 1.09; 95% CI 0.60, 2.00) for an obese ~N(0,σ2)) to each confounder value. The true effect of ascorbic acid on woman who subsequently changed her BMI category from normal since her mortality was zero (OR=1). Simulated scenarios differed on the mutual last pregnancy. correlation between confounders (ρ) and outcome incidence (py). Conclusion: The interpretation of the OR depends on which model is Unmeasured confounding alone yielded little bias: ORs ranged between 1.00- considered. The PA model provides a between women comparison, FE within 1.03. Misclassification of confounders (without unmeasured confounding) woman between pregnancies estimate and RE both within and between resulted in bias, e.g. OR 0.78 (for σ=2, ρ=0.3, and p y=0.2). When women. Choice of which model to use will depend on the objective of the misclassification was present, unmeasured confounding resulted in additional individual study. bias, e.g. OR 0.73 (for σ=2, ρ=0.3, and py=0.2). Although misclassification of confounders can results in substantial bias, our P19.8 example shows that it is implausible that this can fully explain the effects of Use of Scottish Electronic Medical Record Linkage Systems: Illustrated by ascorbic acid on mortality as observed in nonrandomized studies. When WOSCOPS 15 Year Follow-up Data confounders are mutually related, the impact of unmeasured confounding is larger if unmeasured confounding is present in combination with Heather Murray, Ian Ford University of Glasgow, Glasgow, UK misclassification of confounders. P19.6 Propensity score: alternatives to logistic regression - real example Simona Littnerova1, Jiri Jarkovsky1, Jindrich Spinar2, Jiri Parenica2, Marian Felsoci2 1 Institute of biostatistics and analyses, Brno, Czech Republic, 2Department of cardiology, Hospital Brno, Brno, Czech Republic The analysis using propensity score is one of the available methods to modify the non-random distribution of affecting factors in randomized clinical trials. Propensity score are typically estimated by logistic regression, but alternative methods to create propensity score are available. From the public health, biostatistics, discrete mathematics, and computer science literature we identified alternative methods for propensity score estimation, which could have same advantages over logistic regression. We choose two techniques as alternatives to logistic regression: decision trees (classification and regression tree - CART) and random forest. The aim of this work is to compare this method to estimation propensity score and an example of its potential use is given in its subsequent application to data from the Czech database of heart failure and myocardial infarction Registry BRNO. Using the propensity score helped to obtain a balanced dataset. In 2007 the Long-term follow-up of the West of Scotland Coronary Prevention Study (WOSCOPS) was published in the New England Journal of Medicine showing that the risk of coronary heart disease or non-fatal myocardial infarction was 10.3% in placebo group and 8.6% in the pravastatin group (p=0.02) in the period approximately 10 years after completion of the study. Similar percentage reductions were seen for other cardiovascular outcomes, confirming the safety of pravastatin and suggesting an ongoing benefit in reducing CHD events. Monitoring the long term safety of outcome trials is one important use of computerised medical record linkage systems. Here we will illustrate other uses of record linkage systems using WOSCOPS as an example. We will show there is strong correlation between the number of adverse events / deaths reported within WOSCOPS trial with number of hospitalisations and deaths extracted from the electronic hospital discharge and death records. We will discuss the benefits of increased follow-up such as additional power achieved by increased number of events and the potential to remove early events which may be associated with pre existing diseases unknown at baseline such as cancer and diabetes still allowing enough events to show significant association with outcomes. We will give examples of strong associations found between cardiovascular and fatal outcomes with baseline biomarkers and other risk factors such as BMI and heart rate. Time to event analysis and Cox proportional hazard models as well as competing risk models will be used for analysis. ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info P19.9 Posterior Capsule Rupture complication rates for Cataract surgery from 1,173 ophthalmic surgeons in 28 UK NHS trusts. Paul HJ Donachie1, Robert L Johnston1, John M Sparrow2, Irene M Stratton1 1 Gloucestershire Hospitals NHS Foundation Trust, Gloucestershire, UK, 2 University Hospitals Bristol NHS Foundation Trust, Bristol, UK The National Ophthalmology Database (NOD) has collated ophthalmological surgery data from 28 UK hospital NHS trusts that use Electronic Medical Record systems and contains data on 248,764 cataract operations performed by 1,185 surgeons since 01/04/2000. Posterior Capsule Rupture (PCR) is the most common complication of cataract surgery and is of interest as a measure of surgical performance for revalidation. NOD has data on 244,442 cataract operations from 169,779 patients that underwent planned cataract operations performed by 1,173 surgeons and which are eligible for PCR rate comparisons. The mean PCR rate was 1.6% from 276 consultants, 2.1% from 510 independent surgeons, 2.5% from 134 experienced trainees, 4.2% from 253 inexperienced trainees and 1.9% overall. Un-adjusted for case mix funnel plots of all surgeons and by surgical grade will be shown. 113/156 significance of prognostic factors. In 2011, Choodari-Oskooei and colleagues studied 17 R2-type predictive ability measures proposed for survival models. They showed that all the measures have some shortcomings, but their studies overall singled out two measures R2PM, R2D, which are recommendable for use in (censored) survival data. Both measures depend on the variance of the prognostic index (PI) of the model fitted to the data. Here, we propose a new measure and compare it to the ones recommended above. Our proposed measure is an extension of the total gain (TG) statistic, proposed for a logistic regression model, to survival models. It is based on the binary regression quantile plot, otherwise known as the predictiveness curve, and ranges from 0 (no predictive ability) to 1 (‘perfect' predictive ability). We explored the properties of the TG in censored survival data using simulations and real data. We investigated the impact of censoring, covariate distribution, and influential observations. The results of our simulations show that similar to R2PM and R2D, TG is independent of censoring. But unlike R2D, it is affected by the follow-up time and outlying observations. Finally, we applied TG to quantify the predictive ability of prognostic models developed in several disease areas. On balance, TG performs satisfactorily in our empirical studies and can be recommended as an alternative measure to quantify the predictive ability in survival models. Keywords Survival analysis, predictive ability, total gain (TG) measure, predictiveness curve, prognostic models P19.10 Paternal age - and the risk of oral cleft Erik Berg, Øystein Haaland, Rolv Terje Lie P20.2 Department of public health and primary healthcare, University of Bergen, Aggregating published prediction models with individual patient data Norway Debray1, Erik Koffijberg1, Daan Nieboer2, Yvonne Vergouwe2, Karl This study is a prospective, national cohort study, using information from Thomas 1 Moons , Ewout Steyerberg2 Medical Birth Registry of Norway (MBRN) in the period from 1967 to 2010. 1 2 Orofacial clefts, include cleft lip (CL+/-P) and palate only (CP0), are together University Medical Center Utrecht, Utrecht, The Netherlands, Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands the most common craniofacial congenital anomaly. With a worldwide prevalence of 1 per 1200 live births, Norway is among the highest rates of clefts in the western world. Earlier studies of paternal age and the risk of orofacial clefts are not unambiguous. The aim of this study is to test the hypothesis that increased paternal age is associated with a higher risk of having a child with oral clefts. Since 1967 all births in Norway after 16 weeks of gestation have been compulsory reported to the Medical Birth Registry of Norway (MBRN), and registered with a unique personal identification number. Congenital defects detected in this period, is recorded according to the International Classification of Diseases, Eight Revision (ICD-8), on a standard form to the MBRN as a part of the registry. We investigate cleft lip and palate (CLP) isolated from the cleft lip only (CPO) cases. The proportion of clefts registered in the MBRN is known: CLO 83%, CLP 94%, CPO 57%, We used various regression techniques to estimate the effect of father's age on the risk of subgroups of oral clefts. Preliminary results using generalized additive models indicate that an effect of high paternal age may be present for subgroups of cleft lip, but with a much weaker effect for uncomplicated isolated cases. P20 Prediction P20.1 A new measure of predictive ability for survival models Babak Choodari-Oskooei, Patrick Royston, Mahesh KB Parmar MRC Clinical Trials Unit, London, UK Background: Previously published prediction models are often ignored during the development of a novel prediction model. Consequently, numerous prediction models generalize poorly across patient populations, and might have been improved by incorporating such evidence. Unfortunately, aggregation of prediction models is not straightforward, and methods to combine differently specified models are currently lacking. Methods: We propose two approaches for aggregating the previously published prediction models with observed individual participant data. These approaches yield a new explicit prediction model that, once derived, no longer requires the original models. The first approach is based on model averaging and estimates an overall prediction model that weighs the predictions of the literature models. The second approach is based on stacked regressions, and combines the predictions of the literature models in a logistic regression analysis. We illustrate an implementation in two empirical datasets for predicting Deep Venous Thrombosis and Traumatic Brain Injury, where we compare the approaches to established methods for prediction modeling. Results: Results from the case studies demonstrate that aggregation yields prediction models with an improved discrimination and calibration in a vast majority of scenarios, and result in equivalent performance (compared to the standard approach) in a small minority of situations. Conclusions: The proposed aggregation approaches may considerably improve the quality of novel prediction models, and are particularly useful when few participant data are at hand. P20.3 External Validation of a Prognostic Model in Epilepsy: simulation study and case study Predictive ability measures provide important information about the practical Laura Bonnett, Anthony Marson, Paula Williamson, Catrin Tudur Smith 114/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info University of Liverpool, Liverpool, UK Background: Before a prognostic model can be implemented in practice, it should be externally validated. We have published a prognostic model in epilepsy. Now we consider how to externally validate it. Methods: A simulation study was undertaken assessing the performance of statistical methods for externally validating prognostic models, and for handling covariates missing from the validation dataset. An initial systematic review suggested the most common external validation methods were discrimination and calibration. In our simulation study deviance, concordance and Royston's measure of prognostic separation were tested. These methods were also tested on three validation datasets. Our simulation study tested five adaptations of standard methods for handling missing data within covariates to entirely missing covariates: random selection with replacement, hot deck imputation, single imputation via estimation, random selection with replacement multiple times, and only using covariates common to both the development and validation dataset. These methods were then applied to one validation dataset with a missing covariate. Results: In the simulation study concordance showed almost perfect agreement between the similarly simulated development and validation datasets. Variable matching performed poorly. In our case study all five methods of handling missing covariates performed equally. Via the concordance method, the prognostic model for epilepsy generalised well to the validation datasets. Conclusions: Concordance may be a suitable method of external validation together with selected methods of imputation for a missing covariate. Further work is required to determine how these methods perform in alternative settings and how external validation results should be presented in general practice. P20.4 Multiple longitudinal profiles of patients reported outcomes as predictors to clinical status of rheumatoid arthritis patients: A joint modeling approach Siti Haslinda Mohd Din1, Marek Molas1, Jolanda Luime1, Emmanuel Lesaffre1,3 1 Erasmus University Medical Centre, Rotterdam, The Netherlands, 2 Department of Statistics, Wilayah Persekutuan Putrajaya, Malaysia, 3Catholic University of Leuven,L-Biostat, Leuven, Belgium Longitudinal profiles are most often analyzed as a response. However, longitudinal profiles can themselves also be used as predictors for a response. The response can be continuous, categorical, a survival outcome but also univariate and multivariate. In addition more than one longitudinal profile may be used as predictor. In this project, longitudinal profiles of multiple patients reported outcomes (PROs) are used in predicting the clinical status of rheumatoid arthritis patients. Patients completed a monthly PROs questionnaire online from month 0 till month 12. The clinical status of 3 month is measured by the DAS28, which is a physical exam performed by a rheumatologist or nurse. The nature of the PROs data requires assuming a longitudinal model for bounded outcome scores as the first component of the model. Each PRO yields information for estimate of random intercept and slope, which is used in the second stage to predict the value of DAS28. The second stage assumes a normally distributed outcome; therefore a classical normal regression model is used. The aim of this project is to demonstrate the joint model of multiple longitudinal profiles of bounded outcome score as a better prediction for rheumatoid arthritis patients' clinical status. We use a maximum likelihood as well as a Bayesian approach. All results are validated using K-fold cross validation technique. P20.5 Using Dynamic Regression and Random Effects Models for Predicting Hemoglobin Levels in Novel Blood Donors Kazem Nasserinejad1, Wim de Kort3, Mireille Baart3, Arnost Komarek4, Emmanuel Lesaffre1,2 1 Department of Biostatistics, Erasmus MC, Rotterdam, The Netherlands, 2LBiostat, Catholic University of Leuven, Leuven, Belgium, 3Sanquin Blood Supply, Nijmegen, The Netherlands, 4Faculty of Mathematics and Physics, Department of Probability and Mathematical Statistics, Charles University, Prague, Czech Republic In several Western European countries blood donation is done on a voluntary basis. In order to optimize the planning of blood donation but also to continue motivating the volunteers it is important to streamline the practical organization of the timing of donation. Donation may, however, be declined and this for a variety of reasons. A common reason is a too low hemoglobin level of the donor which means that the hemoglobin level is below 8.4 mmol/l for men and below 7.8 mmol/l for women. We wish to predict the future hemoglobin value in order to better decide when donors can present themselves for donation. The development of the hemoglobin prediction rule is based on longitudinal data from blood donations collected by the Sanquin blood supply in the Netherlands. We explored and contrasted two popular statistical models, i.e. the transition (Markov) model and the random intercept model as plausible models to account for the dependence among subsequent hemoglobin levels within a donor. The aim of this exercise is to ascertain which of the two models is better in predicting future hemoglobin values. We showed that the transition (Markov) model and the random intercept model have almost the same prediction accuracy at the first donation but for longer series the transition model offers a better prediction. At this moment we assumed equal time-intervals between subsequent visits. We currently are exploring the extension to unequal time-intervals and the comparison to alternative prediction models P20.6 Time series clustering based on nonparametric multidimensional forecast densities: An application to clustering of mortality rates Jose A. Vilar, Juan M. Vilar University of A Coruña, A Coruña, Spain Time series clustering is nowadays an active research field with a broad range of applications. The choice of a suitable dissimilarity measure between two series is a key issue. However, the notion of similarity between series is nontrivial and can be established in different ways. We propose a notion of dissimilarity governed by the performance of future forecasts. Specifically, the dissimilarity between two series is measured by means of the L1 distance between their corresponding forecast densities for a sequence of k pre-specified time horizons. Comparing the forecast densities instead of the point forecasts allows us to separate into different clusters time series having similar predictions but different generating models. Our clustering algorithm is outlined as follows. Step 1. Obtaining samples of bootstrap prediction vectors of length k for each series. Step 2. Determining a low-dimensional space where the bootstrap predictions are projected. Step 3. Estimating the multidimensional density associated with each set of projections. Step 4. Obtaining a pairwise dissimilarity matrix by computing the distance between each pair of densities. Step 5. Performing an standard hierarchical clustering algorithm based on the dissimilarity matrix constructed in Step 4. Our clustering methodology is applied to a collection of series representing annual mortality rates for different countries. Our objective is to group these countries according to their mortality predictions on different future time ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info 115/156 periods. Note that the prediction of future mortality rates is a problem of Additionally, we found that poor within cluster concordance was strongly fundamental importance for the insurance and pensions industry. related to poor between cluster concordance. When assessing discriminative ability in clustered data, the within cluster concordance probability is useful to evaluate clinical models, whereas the P20.7 Time-dependent ROC curves for the estimation of true prognostic capacity of overall concordance probability is favourable to evaluate public health models. Furthermore, heterogeneity in concordance among clusters should be taken microarray data into account. 1 2 Yohann Foucher , Richard Danger 1 EA 4275 Biostatistics, Clinical Research and Subjective Measures in Health P20.9 Sciences, Nantes University and Transplantation, Urology and Nephrology Institute (ITUN), INSERM U1064, Nantes, France, 2Transplantation, Urology Modelling crown-rump length (CRL) data used for prediction of gestational age in early pregnancy when the data is truncated at both ends: The case study of and Nephrology Institute (ITUN), INSERM U1064, Nantes, France INTERGROWTH-21st Project Microarray data can be used to identify prognostic signatures based on time-to1 2 event data. The inherent problem of dimension (number of features i.e. genes Eric Ohuma , Doug Altman 1 Nuffield Department of Obstetrics & Gynaecology, and Oxford Maternal & >> number of individuals) causes overoptimistic results. of We propose a bootstrap 0.632+ estimator of the area under the time- Perinatal Health Institute (OMPHI), Green Templeton College, University 2 Oxford, Oxford, OX3 9DU, UK: for the International Fet, Oxford, UK, Centre for dependent Receiver Operating Characteristic (ROC) curve. Cox model with lasso penalty is used for the selection of features. The complete re-estimation Statistics in Medicine, University of Oxford, Wolfson College Annexe, Linton of the model is performed at each iteration, including the value of the tuning Road, Oxford OX2 6UD, UK., Oxford, UK parameter. We propose an R package entitled ROCt632 available at www.divat.fr/en/softwares. We validated the proposed methodology by simulations and comparisons with other methods: cross-validation, bootstrap, bootstrap cross-validation and bootstrap 0.632. We applied the methodology to a public microarray dataset, which includes 240 patients with diffuse large b-cell lymphoma and 7,399 features. Depending on the prognostic time, the area under the curves (AUC) obtained by using the 0.632+ estimator were between 0.70 and 0.65. This illustrates the utility of this signature to predict mortality up to 15 years, but it also illustrates that this signature alone is not sufficient for medical decisionmaking. ROC-based interpretations are well-accepted in the community of biologists and clinicians. Thus, the 0.632+ estimator of time-dependent ROC curve constitutes a useful method to establish and communicate the predictive accuracy of prognostic signature based on high-dimensional data. Fetal growth charts are important for monitoring a child's growth and development over time. Fetal crown-rump length (CRL) is measured in early pregnancy primarily to determine the gestation age (GA) of a fetus. An estimate of gestational age can be reliably obtained from women with a regular 28-32 days menstrual cycle and who know their first day of their last menstrual period (LMP). The CRL is most reliable for estimating GA between 9 and 14 weeks gestation, using a formula developed in 1975. The INTERGROWTH-21st Project includes 4000 women with a regular cycle (28-32 days) whose LMP and CRL estimates of GA agreed within 7 days. We aim to develop a new CRL centile chart for estimating GA. The main statistical challenge is modelling data with the outcome variable (GA) truncated at both ends, i.e. at 9 and 14 weeks. The data span only 5 weeks so using only CRL data unaffected by truncation leads to a large loss of data and limited clinical usefulness. One method is first to create a model for predicting CRL from GA. We assume that the CRL values are conditionally normal at any given GA and apply fractional polynomial regression to the mean and SD. We can use the model to P20.8 predict GA from CRL or as a basis for imputing the missing values. We will Assessing discriminative ability in clustered data present analyses using these approaches. We will also consider the David van Klaveren, Yvonne Vergouwe, Ewout W. Steyerberg consequences of including only women whose estimated GA was within 7 days Dept of Public Health, Erasmus University Medical Centre, Rotterdam, The between CRL and LMP. Netherlands For clustered data little effort has been put into assessing the discriminative P20.10 ability of prognostic models. Van Oirbeek and Lesaffre recently proposed an Temporal profile of time-dependent discrimination measures in survival adaptation of the concordance probability to multilevel regression models. We analysis aimed to study the practical use of the concordance probability as a measure Jerome Lambert, Sylvie Chevret of discriminative ability in clustered data. Univ Paris Diderot, Sorbonne Paris Cité; INSERM, UMR 717; AP-HP, Hop We view the within cluster (e.g. center) concordance probability, representing Saint Louis, Service de Biostatistique et Information Médicale, Paris, France concordance of pairs of patients belonging to the same cluster, as an appropriate discrimination measure in clinical practice, because decisions are Introduction taken at the single center (cluster) level. In public health applications we prefer Prognostic studies are usually constructed with the use of survival models. To the overall concordance probability, also taking between cluster concordance evaluate the performances of these models, the concept of discrimination, into account, because decisions are taken at the population level. We illustrate which is well described in the diagnostic framework with logistic models (Cour findings with clinical survival outcome data after traumatic brain injury statistic), has been extended to survival analysis. Thus, several time(6,691 patients clustered in 224 centers) and with binary public health outcome dependent C-statistics have been proposed which can be computed at a given data of a Chlamydia screening program (80,380 participants clustered in 183 time point of the follow-up. Temporal profile of these discrimination measures need to be explored to detangle the complex relationships between evolution of neighbourhoods). predictive ability over time and statistical property of the time-dependent cThe TBI case study shows that concordance probability estimates can vary statistics. substantially among clusters (IQR 0.69-0.85). Because of the observed 2 heterogeneity (I = 0.72) we used random effects meta-analysis to derive Methods means and prediction intervals for the within cluster concordance probability. Simulation studies were conducted to mimic several scenarios with different 116/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info prognostic multiplicative impact of covariates on baseline hazard, and constant, increasing, or decreasing hazard ratios over time, and different censoring rates. Temporal profile of the various C-statistics computed as a function of time was assessed. Finally, an example on real data was conducted to assess the prognostic value of three biomarkers in a cohort of 5306 patients hospitalized for acute heart failure. Results Our simulations show that when the prognostic value is non-null, even when the hazard is constant, the discriminative ability across time improves. Results of the simulations also illustrate the impact of the violation of the proportional hazard hypothesis on the shape of discrimination over time. Conclusion Various C-statistics have been proposed to assess discriminative ability of a prognostic model over time. Our study suggests that the shape of discriminative ability over time can be counter-intuitive and should be cautiously interpreted. developed on 2037 patients from oncology referral centers (65% benign, 7% borderline, 29% invasive tumors) and validated on 1107 patients from public hospitals (prevalences 83%, 4%, and 13%, respectively). The validation data were splitted into updating (n=945) and testing sets (n=162). Sequential dichotomous modeling is used to obtain a polytomous prediction model: the first model predicts benignity, the second distinguishes borderline from invasive tumors. To obtain the original model, backward variable selection was performed on seven variables preselected based on expert knowledge. We considered recalibration and revision updating methods, and two reference methods (no updating and redevelopment). Discrimination and calibration was studied based on 500 splits of the validation data. No updating method improved the discrimination on the testing data, with redevelopment even showing a decrease. This decrease was stronger when smaller updating set sizes were used. Calibration was greatly improved even by simple recalibration methods. Under redevelopment, variable selection results were unstable and coefficients overfit. We conclude that simple dichotomous updating methods behaved well when applied to polytomous models. When sample size in a new setting is small, simple updating may be preferred over model redevelopment. P20.11 Validation of risk prediction models for clustered data: A simulation study and practical recommendations P20.13 Rumana Z Omar, Shafiqur Rahman, Gareth Ambler Probability of survival for very preterm births: production and validation of a University College London, London, UK prognostic model. Clustered binary and survival outcomes commonly arise in health research, for Bradley Manktelow, Sarah Seaton, David Field, Elizabeth Draper example, in multicentre studies. Although clustering is accounted for in explanatory models, it is usually ignored in risk prediction models, particularly Department of Health Sciences, University of Leicester, Leicester, UK in their validation. This work extends some of the existing validation measures Accurate estimates of the probability of survival of very preterm infants for use with random effects logistic and Cox frailty models. These are: Harrell's admitted to neonatal care are vital for counselling parents, informing care and C-index, D statistic, Gonen and Heller's K index and the calibration slope (CS). planning services. In 1999, a prediction model for the probability of survival by Two approaches are proposed, producing an overall validation measure across gestation, birthweight and gender was published using UK data from The all clusters and a weighted average of cluster-specific measures. To calculate Neonatal Survey (TNS). This model is widely used in clinical care but the overall measure, predictions can include the fixed predictors and the improvements in survival required that it be updated. random effects (P(re)), or assume random effects as zero (P) or consider a 2,995 white singleton infants born at 23+0 to 32+6 weeks gestation from 2008marginal prediction by integrating out the random effects (P(pa)). Their 2010 were identified from TNS. A logistic model was fitted with gestation, performances were assessed through simulations by estimating bias and mean birthweight and gender as predictors. Non-linear functions were estimated by squared errors of the validation measures and the coverage of confidence fractional polynomials. Bootstrap methods, with 500 repetitions, were used to intervals. The simulation scenarios were varied by number of clusters, cluster investigate need for interactions and assess internal validity of the final model size, intra-cluster correlation coefficient (ICC) and proportion of censoring for by monitoring the c-statistic and Cox regression coefficients. Discrimination survival outcomes. Validation measures using the P(u) approach showed and calibration of the final model were assessed through the c-statistic, Cox reasonable performance when cluster size was large and those using the P regression coefficients, Farrington's statistic and Brier score on the entire and P(pa) approaches performed poorly for moderate values of ICCs as they dataset and clinically relevant subsets. ignore clustering. The D-statistic and CS performed well for all simulation scenarios, except for small cluster sizes. The K index generally performed well. A final prediction model was obtained: c-statistic=0.86; Farrington p=0.44. The C-index did not perform well particularly in presence of censoring. Care is Predicted survival ranged from 4% to 99%. The bootstrap estimates indicated needed when validation data is from clusters different from those used in excellent overall discrimination (mean c-statistic: 0.86, range: 0.85, 0.86). The bootstrap Cox calibration coefficients were found to have a mean intercept of model development. 0.014 (range: 0.44, 0.43) and a mean slope of 0.99 (range: 0.84, 1.21) indicating good calibration. P20.12 These internationally validated survival charts have been updated using robust Updating of polytomous risk prediction models based on sequential methodologies to ensure adequate predictive performance. Use of fractional dichotomous modeling improves the performance polynomials offers a straightforward methodology to produce a simple and Kirsten Van Hoorde1, Yvonne Vergouwe2, Dirk Timmerman1, Sabine Van clinically plausible model. Huffel1, Ewout W Steyerberg2, Ben Van Calster1 1 KU Leuven, Leuven, Belgium, 2Erasmus Medical Center, Rotterdam, The P20.14 Netherlands Using Bayesian model averaging to improve radiation-induced cancer risks When a dichotomous prediction model performs poorly in a different setting, predictions model updating is often a sensible approach. This may be particularly relevant Sophie Ancelet, Olivier Laurent, Neige Journy, Dominique Laurier for polytomous outcomes. We demonstrate the use of dichotomous updating methods for polytomous models developed using sequential dichotomous IRSN/LEPID, Fontenay-aux-Roses, France modeling. The prediction of cancer risks, arising from low doses and dose rates exposure We consider a case study on ovarian tumor diagnosis. The original model is to ionizing radiation, has been an endeavour that has increased in magnitude ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info and effort over the last century. The Japanese atomic bomb survivor Life Span Study (LSS) cohort, even if exposed at middle-to-high doses and high dose rates, is the radiation epidemiology dataset most currently used to assess radiation-induced cancer risks and extrapolate cancer risks to other populations. Among the most problematic sources of uncertainty when predicting risks from this approach are model and parameters uncertainties. Particularly, the choice of link functions as well as explanatory variables all contributes to uncertainty in the construction of relevant dose-response relationships. Neglecting such uncertainties is a serious shortcoming of most of previous risk assessments. We therefore investigate the possibility of using Bayesian model averaging (BMA) to simultaneously reflect the impact of these two sources of uncertainty in risk predictions. Our motivating case study involves the prediction of the proportions of childhood leukaemia incidence that may be due to naturally occurring radon gas in France. We used six recently published risk models for radiation-induced leukaemia. We applied MCMC algorithms as implemented in WinBUGS to re-fit the competing models to the LSS cohort data. We computed the posterior model probabilities and carried out BMA in this context. Our first predictive results suggest that a small still sizeable percentage of childhood leukaemia cases might be attributable to radon in France and that uncertainty in the predictions might decrease with attained age. 117/156 recovery rates. Our aim is to use images for selection of highly discriminative brain regions between schizophrenia patients and healthy controls and predict an outcome of patients one year after the first schizophrenia episode based on the selected brain areas. METHODS. Deformations of 3-D magnetic resonance images were used as an input into an algorithm for selection of brain regions which are highly discriminative between 52 schizophrenia patients and 52 controls. The algorithm consists of a combination of penalised regression and a resampling method. This procedure aims to calculate selection probabilities of each image voxel by repeatedly fitting the penalised regression model on random subsets of the data set, while keeping track of voxels selected in each iteration. The final set of discriminative brain regions contains voxels with selection probability higher than 0.5. The brain regions are then used in prediction of a good or poor outcome of schizophrenia patients (Global Assessment of Functioning Scale >70 and <70, respectively) using linear discriminant functions. RESULTS. A total of 30,461 highly discriminative voxels were selected. Crossvalidated accuracy of discrimination between patients and controls based on the voxels was 85.6%. Outcome prediction accuracy was equal to 63.5%. CONCLUSION. The results compare favourably to those obtained by current state-of-the-art methodologies. Further work will aim to increase the prediction accuracy with using nonlinear methods for prediction. P20.15 Assessment of risk prediction and individualised screening of breast cancer P20.17 among Swedish postmenopausal women Meta-analysis methods for examining the performance of a predictive test: going beyond the average Hatef Darabi, Keith Humphreys Ikhlaaq Ahmed1, JP Noordzij1, Jon Deeks2, Lucinda Billingham1, Richard Riley2 Karolinska Institutet, Stockholm, Sweden 1 Methodology Research at the University Over the last decade several breast cancer risk alleles have been identified Based in MRC Midland Hub for Trials 2 which has led to an increased interest in individualised risk prediction for of Birmingham, Birmingham, UK, Affiliated with MRC Midland Hub for Trials clinical purposes. In the present work we examine several models for Methodology Research at the University of Birmingham, Birmingham, UK predicting absolute risk, in particular we examine the performance of an up-todate 18 breast cancer risk single-nucleotide polymorphisms (SNPs), together with mammographic percentage density (PD), body mass index (BMI) and clinical risk factors in predicting absolute risk of breast cancer, utilising the Gail approach. Adding mammographic PD, BMI and all 18 SNPs to a baseline Swedish Gail model improved the discriminatory accuracy (the AUC statistic) from 55% to 62%. The net reclassification improvement (NRI) was used to assess improvement in classification of women into low, intermediate, and high categories of 5-year risk, where significant positive reclassification was observed (NRI= 0.170). Using published effect estimates for the 18 markers and the clinical variables we evaluate several approaches to individualised screening, against age only-based screening, in women aged 40 to 75 years. We show, for the Swedish female population, that a personalised screening approach based on a risk prediction model incorporating age, Gail model variables, PD, BMI and 18 SNPs captures significantly more breast cancer cases than screening approaches using equal resources based on age and Gail model variables and on age alone. Taken together, genetic risk factors and mammographic density offer moderate improvements to clinical risk factor models for predicting breast cancer. A predictive test is a single factor that accurately predicts individual outcome risk for patients with a particular condition. For example, in patients with a thyroidectomy, parathyroid hormone (PTH) measured at between 1 to 6 hours post-surgery predicts which patients will become hypocalcemic within 48 hours. Often multiple studies examine the predictive accuracy of a particular test, and meta-analysis is then required. In this talk, using data for 9 studies examining the predictive accuracy of PTH, we describe the use and clinical interpretation of a bivariate random-effects meta-analysis that synthesises the evidence about PTH’s sensitivity and specificity. This model accounts for between-study heterogeneity and correlation in sensitivity and specificity, and leads to estimates of the average sensitivity and specificity. Though such results are informative, they do not easily translate to clinical practice, as test performance in a single clinical setting may differ substantially from the average. To better understand the impact of using the average sensitivity and specificity in clinical practice, we discuss three possible approaches: (i) calculate predictions intervals to reveal how the test’s true sensitivity, specificity and c-statistic may vary from their average in individual settings; (ii) take the average sensitivity and specificity estimates, then in each study combine with the observed prevalence to obtain positive and negative predictive values, and assess study calibrations (predicted events (E) vs. observed events (O)); (iii) assume a new settings true prevalence is unknown, and repeat (ii) but use the average study prevalence across studies, and check calibration. P20.16 Outcome prediction in schizophrenia patients based on image data Eva Janousova1, Daniel Schwarz1, Tomas Kasparek2 P20.18 1 Institute of Biostatistics and Analyses, Masaryk University, Brno, Czech How to assess discrimination performance of polytomous prediction models: Republic, 2Department of Psychiatry, Faculty of Medicine, Masaryk University, review and recommendations Brno, Czech Republic Ben Van Calster1, Yvonne Vergouwe2, Vanya Van Belle1, Caspar Looman2, INTRODUCTION. Recently, there is an effort to use image data for support of Ewout W. Steyerberg2 schizophrenia diagnostics. Improved detection of schizophrenia and early 1 KU Leuven, Leuven, Belgium, 2Erasmus MC, Rotterdam, The Netherlands intervention with individual treatment strategies would increase patient 118/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info Discrimination of risk prediction models for dichotomous outcomes is typically evaluated with the c-statistic. The evaluation of prediction models for polytomous models is less straightforward. Several extensions of the c-statistic to nominal and ordinal outcomes have been suggested in the literature. We review and suggest tools to assess discrimination of polytomous prediction models, with illustration in a case study of 1094 men with testicular cancer. For a first assessment, a "discrimination plot" is attractive, which shows box plots of estimated risks for each outcome category by actually observed category. Outcome category prevalences are indicated with horizontal lines. Then, we suggest to summarize the overall performance with a polytomous c-statistic, such as the Polytomous Discrimination Index (PDI) for nominal outcomes. Although some measures weight by prevalence, we advice unweighted discrimination measures. Misclassification costs and prevalence should be considered in a later phase, when focusing on clinical usefulness of the model. If the polytomous index suggests discriminatory ability, this may further be investigated with pairwise c-statistics. Given that the risks for a pair of categories, P(A) and P(B), do not sum to one for polytomous outcomes, pairwise c-statistics for nominal outcomes can be obtained in several ways. We suggest the "conditional risk" method, which computes the standard c-statistic using P(A) divided by P(A)+P(B). This method is consistent with the standard multinomial logistic regression model. In conclusion, several tools can be recommended for the assessment of polytomous discrimation. Further research is needed on other performance aspects such as calibration and clinical usefulness. P20.19 Comparison of two different modelling techniques to determine parameters related to changes in quality of life in colorectal cancer patients Jose M. Quintanta 1, Urko Aguirre1, Nerea Gonzalez1, Marisa Bare2, Santiago Lazaro1, Cristina Sarasqueta3, Eduardo Briones4, Antonio Escobar5 1 Hospital Galdakao-Usansolo, Galdakao, Bizkaia, Spain, 2Corporacio Parc Tauli, Barcelona, Spain, 3Hospital Donostia, Donostia, Gipuzkoa, Spain, 4 Hospital de Valme, Sevilla, Spain, 5Hospital Basurto, Bilbao, Bizkaia, Spain Objectives: to develop and compare different statistical models to predict short term change in health related quality of life in patients undergoing curative surgery by colorectal cancer. Methods: Prospective cohort study of patients diagnosed of colorectal cancer who underwent curative surgery in any of the 7 participant hospitals. Patients were followed from the moment they got in touch with the index hospital at admission and with a follow-up at 30 days after discharge. Patient sociodemographic (age, gender), clinical (TNM stage, localization, complications) variables were retrieved from the patient medical record. Additionally, patients fulfilled the EuroQol-5D questionnaire before surgery and at 30 days after discharge. Two different multivariate models were developed: a general linear model (GLM) model and hierarchical linear models (HLM). They were compared in terms of beta estimates, standard errors, and statistical significance of the considered variables in the final model. Results: Based on the multivariate GLM model, he presence of complications during the admission, the length of stay in the hospital, and the basal EuroQol5D score were correlated with changes in the EuroQol-5D. The multivariate multilevel model found also that those three variables were related to the changes in the EuroQol-5D scores but found that gender was related as well. Conclusions: Though both models showed almost similar information, the HLM model seems to provide with additional statistical significant variables (gender) of great importance from a clinical and health services research point of view. Determining which model is more appropriate and accurate is essential in this study. P20.20 Predictive performance of random forest based on pseudo-values Ulla B Mogensen, Thomas A Gerds University of Copenhagen, Copenhagen, Denmark Random forest is a supervised machine learning method that combines many classification or regression trees for prediction. Here we describe an extension of the random forest method for building event risk prediction models in survival analysis with competing risks. With right-censored data the event status at the prediction horizon will be unknown for some subjects. We propose to replace the censored event status by a jackknife pseudo-value, and then to apply an implementation of random forests for uncensored data. In a simulation study the predictive performance of the resulting pseudo random forest is compared to that of random survival forests and of combined causespecific Cox regression model. Performance is measured with adoptions of Brier scores, AUC and the C-index to survival data. We apply the pseudo random forest to predict the risk of death caused by cardiovascular diseases for stroke patients in the Copenhagen stroke study. P20.21 Finding cut-offs for continuous prediction models: an overview of methods and pitfalls Verena Sophia Hoffmann Ludwig-Maximilans-Universität, München, Germany The prediction of outcomes is a challenge occurring in all areas of medicine. It is important for therapy selection, stratification in clinical trials, economic evaluations and patient information. Depending on number and properties of predictive factors and statistics used the result either has many values or is continuous. Often this is not useful in clinical practice and classification of predictions is asked for to enable decision making. A wide range of methods can be used to find appropriate cut-offs: A simple way can be to use mean or median predictions, the minimal p-value approach aims to maximize the adequate test statistic, converging tangents of different slopes to the ROC curve incorporates different costs of outcomes and tree based models can be grouped by pruning and pooling. Assets and drawbacks of these methods will be discussed using appropriate examples. Most of the approaches to find a cut-off are data driven. For this reason it is likely that the classification works better in the data used to find the cut off than in new data. Therefore the model itself together with the cut-off needs to be validated. Usage of methods of internal validation, like cross validation and bootstrap validation, and external validation, as validation in different study groups or settings, are presented and discussed. Also different approaches to internal and external validation via the split-sample approach are shown. The methods are illustrated with applications to data of patients from the European Treatment and Outcome Study for CML (EUTOS). P20.22 Validating Prediction Models in Small Datasets Gareth Ambler, Bridget Candy, Michael King, Rumana Omar University College London, London, UK Logistic regression is often used to investigate the relationship between a binary outcome and a number of predictors. However such models can have overfitting problems in small datasets. This is the case with data derived from a systematic review of 67 trials, where the binary outcome was treatment compliance and there were 25 factors. The aim of the study was to investigate whether some or all of these factors could be used to design an ‘optimal' new trial. To alleviate overfitting, estimation may be performed using a shrinkage method ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info such as ridge or lasso estimation; backwards elimination is often used in practice. However validating the resulting models can be difficult in such small datasets. For example, data-splitting further reduces the size of the dataset used for model development, and K-fold cross-validation produces K values of the performance measures which may not straightforward to interpret. In addition, the use of several performance measures, including the ROC area, Dstatistic, and calibration slope, can produce contradictory findings in practice. In this research we used simulation based on the review data to evaluate these approaches to validation. Specifically, we simulated new outcomes based on a range of scenarios; these include scenarios with a) no true predictors and b) just a small number of true predictors. Different models (ridge, lasso, full, backward elimination) were fitted then validated, and comparisons made between their true performance and their apparent performance in the validation exercise. Recommendations are made regarding the use of such validation methods in practice. P20.23 Improving prognostic model development and assessment for survival data Paola M.V. Rancoita1,2,3, Cassio P. De Campos2,3, Francesco Bertoni3,4 1 University Centre of Statistics for Biomedical Sciences (CUSSB), Vita Salute San Raffaele University, Milan, Italy, 2Dalle Molle Institute for Artificial Intelligence (IDSIA), Manno, Switzerland, 3Institute of Oncology Research (IOR), Bellinzona, Switzerland, 4Oncology Institute of Southern Switzerland (IOSI), Bellinzona, Switzerland Prognostic models are often developed and used in medicine to stratify patients for survival prediction based on clinical and/or biologic variables. One of the most important aims is to identify patients with very poor outcome, that might take advantage from experimental therapies, or with very good prognosis who might be treated with less intensive regimens. Survival trees and their generalizations are powerful in survival prediction, but they do not account for a core property of prognostic indices: different combinations of causes (values of clinical covariates) can lead to similar phenotype/survival outcome. Few procedures have been suggested for merging the groups corresponding to leaves (combinations of causes) in survival trees, but they show limitations. We propose to apply a clustering algorithm on the survival curves of leaves' groups, using a proper dissimilarity measure. We study several choices for both the clustering algorithm and the dissimilarity measure and we compare them with standard procedures in the literature, using simulated and public real data. In order to enhance the evaluation of the performance of the prognostic models, we also derive a new index of separation to be used together with an error measure of the prediction. This new separation index takes into account three important characteristics of risk groups (besides the prediction itself): the retention of their order, the reliability/robustness in terms of size, and the goodness of separation among all corresponding survival curves. We discuss the new separation index and how other widely used indices miss to capture all these characteristics. 119/156 prognosis assignments. Nevertheless, in latter decades, Machine Learning methods - like Neural Networks (NN) or Support Vector Machines (SVM) - have been introduced to improve the accuracy of the predictive models. This study compares the predictive ability of the developed LR, NN and SVM models for the prognosis of the colorectal cancer stage. Methods A set of 749 of patients with colorectal cancer who underwent surgical intervention was divided into the training and test groups (n = 501 versus n = 248). As output variable patient's colorectal stage was considered. From the data in the training group, an optimal model for prognosis of advanced cancer stage (III-IV versus 0-I-II) was developed with the LR approach. Patient's symptoms, laboratory test and cancer specific antigens were considered as input variables. From the data of the test group, the areas under the receiver operating characteristic (ROC) curve (AUC), specificity and sensitivity of LR, NN and SMV methods have been evaluated. Results Potassium, abdominal pain, hemoglobin and cancer specific antigen were found significant predictors. AUC value from NN was the highest (0.77 vs 0.73 for SVM and 0.71 in LR, p<0.001) and NN showed best screening parameters (sensitivity=61%, specificity = 78%). Conclusions The performance of NN is superior to SVM and LR in the prognosis of advanced colorectal cancer stage. P20.25 Dynamic updating of prediction models: how to deal with heterogeneity between settings Daan Nieboer1, Yvonne Vergouwe1, Ruud G. Nijman2, Rianne Oostenbrink2, Ewout W. Steyerberg1 1 Department of Public Health, Erasmus MC, Rotterdam, The Netherlands, 2 Department of General Paediatrics, Erasmus MC-Sophia Children's Hospital, Rotterdam, The Netherlands With the implementation of prediction models in clinical decision support systems, data on new patients can be collected continuously. This makes it feasible to continuously update prediction models for new settings for which dynamic update methods are required. Various degrees of heterogeneity between the development and the new setting can exist. We consider three different scenarios: coefficients are identical in the development and new setting, only the intercept is different in the new setting, or the strength of individual predictor effects are different. We consider three dynamic updating strategies when new data becomes available: updating the intercept, logistic recalibration (re-estimating the intercept and slope of the linear predictor) and re-estimating the individual regression coefficients. All strategies were applied with Bayesian and Frequentist approaches. Empirical datasets formed the motivating example for generating simulated datasets. A prognostic model was developed and updated for children admitted P20.24 to the emergency care with fever and suspected of a serious bacterial infection. Comparison of Logistic Regression and Machine Learning methods: an Updating is performed until 10.000 patients are included. application to the Colorectal Cancer stage prognosis. Simulation results show that the adjustments in model parameters and model Urko Aguirre1, Jose Maria Quintana1, Nerea Gonzalez1, Antonio Escobar2, performance with increasing sample size occur more smoothly in the Bayesian 3 Cristina Sarasqueta approach, while the Frequentist approach adjusts the model faster in 1 Hospital Galdakao-Usansolo, Galdakao, Spain, 2Hospital Basurto, Bilbao, heterogeneous settings. Hence, there is a trade-off in speed of adjustment and Spain, 3Hospital, Donostia, Spain smooth changes, which requires subject knowledge to decide between a Frequentist or Bayesian approach. Introduction The development of accurate prediction models is important in choosing adequate treatment plans. Logistic regression (LR) models are the most popular methods in a variety of medical domains for performing diagnostic and 120/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info P21 Statistics for epidemiology study planning. P21.1 P21.3 Re-sampling methods in prevalence and incidence studies Exploring the Quality of Life in Patients with Suspected Heart Failure Abdel Douiri1 J Zhang1, J Hobkirk1, S Carroll2, P Pellicori1, AL Clark1, JGF Cleland1 1 King’s College London, London, UK, 2NIHR c-BRC, Guy’s and St. Thomas’ 1 The University of Hull, Hull, UK, 2The University of Leeds, Leeds, UK NHS Foundation Trust, London, UK Age is one of the major determinants of the prevalence and the incidence of Background: The First EuroHeart Failure Survey Questionnaire (EHFSQ-1) most diseases. There are major discrepancies in the underlying age structures has 39 questions on symptoms and quality of life (QoL). Many questionnaire of the registry populations of disease occurrence in each country, and it is items are likely to be highly related. therefore essential that prevalence and incidence studies be assessed Aims: To identify underlying symptom factors among EHFSQ-1 questions and independent of the age profile of each population. Direct standardization is a to observe if the symptom factors affect the construction of an overall "quality fundamental tool used to control the effect of the age structures between of life score" from the questionnaire. populations when making a comparison of different rates. Traditional statistical Methods: Patients referred with symptoms suggestive of heart failure (HF) methods of computing standardized rates and rate ratios in these studies rely between 2000 and 2009 were asked to complete the EHFSQ-1 as a standard on a number of assumptions to estimate confidence intervals for obtained component of clinical assessment. Two patient groups were examined: those estimates. A common assumption is that the rates are distributed as a weighted with HF and those without HF. Exploratory factor analysis based on the sum of independent Poisson random variables which may not be fully justified principal component technique with a Varimax rotation method was used to for a series of periodic overlapping studies. To overcome this problem, we identify patterns of QoL questions and the factor scores were calculated. The recommend a resampling technique based on nonparametric bootstrapping, to 10-fold cross-validation was used to assess the stability of the analysis. calculate confidence intervals of directly standardized rates with at least 5000 Results: Of 1031 patients, 64% were men and the median age was 71 (IQR: replications. These methods are distribution-free and no assumptions are 63-77) years. 626 had HF and 405 did not. For patients with HF, seven required regarding the unknown population probability distribution. The symptom factors were identified: "breathlessness", "psychological distress", proposed method and other common confidence interval procedures for direct "sleep quality", "frailty", "cognitive/psychomotor function", "cough" and "chest standardized incidence or prevalence including methods based on binomial, pain", which accounted for 65% of the total variance. Patients with HF or high poisson and gamma distributions were investigated through Monte Carlo NT-proBNP were more symptomatic. A weighted symptom factor score was simulations and on real data from the South London Stroke Register. tightly correlated with a summary QoL score (r=0.99 with p<0.001 and shown on the scatter plot). P21.2 Conclusions: Using the EHFSQ-1, we found 7 symptom factors in patients with HF. Either a weighted symptom factor score or a summary score can be Development of new Austrian height and weight references used as a QoL outcome measure. Andreas Gleiss1, Gabriele Häusler2, Michael Schemper1 1 Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria, 2Department of Paediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria, 3on behalf of APEDÖ (Working Group on Paediatric Endocrinology and Diabetology, Austria), Austria, Austria In the clinical evaluation of growth disorders and growth monitoring, sex and age dependent height reference curves derived from a relatively small and special sample of Swiss children between 1954 and 1970 have been in use in Austria so far. Due to the secular trend and potential national differences new reference curves needed to be developed on a broad national basis. For this purpose a sample of nearly 14,500 children and adolescents between 4 and 19 years of age was drawn via schooling institutions. Sampling was stratified by provinces according to age and sex specific population proportions. An existing R implementation for Generalized Additive Models for Location, Scale and Shape (GAMLSS) was employed for estimating percentile curves. The flexible Box-Cox Power Exponential distribution was used to describe the height or weight distribution, respectively, at each age while the dependence of the four distributional parameters on age was modelled by cubic spline functions. The degrees of freedom for these splines were selected by an optimization procedure using information criteria. The functional dependence was further adapted, if necessary, according to paediatric or goodness-of-fit considerations. Various points in the basic estimation process of reference curves are considered and two novel refinements are presented: first, we introduce a correction to appropriately take into account the removal of extreme observations before a GAMLSS estimation. Second, we demonstrate the quantification of the uncertainty in extremely low percentiles (such as 0.5%, crucial in diagnostics) based on the bootstrap. This may be valuable for future P21.4 Cultural vs. Clinical characteristics and health-related quality of life of patients with primary liver cancer by using the EORTC QLQ-C30 and the EORTC QLQHCC18 Wei-Chu Chie1, Jane Blazeby2, Chin-Fu Hsiao3, Herng-Chia Chiu4, Ronnie Poon5, Naoko Mikoshiba6, Gillian Al-Kadhimi7, Nigel Heaton7, Jozer Calara7, Peter Collins8, Katharine Caddick8, Anna Costantini9, Valerie Vilgrain10, Ludovic Trinquart11, Chieh Chiang3 1 Institute of Epidemiology and Preventive Medicine, Department of Public Health, College of Public Health, National Taiwan University, Taiwan., Taipei, Taiwan, 2School of Social & Community Medicine, University of Bristol, UK., Bristol, UK, 3Division of Clinical Trial Statistics, Institute of Population Health Sciences, National Health Research Institutes, Taiwan, Chunan, Taiwan, 4 Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Taiwan, Kaohsiung, Taiwan, 5Department of Surgery, The University of Hong Kong, Queen Mary Hospital, Hong Kong, Hong Kong, China, 6Department of Adult Nursing / Palliative Care Nursing, Graduate School of Medicine, the University of Tokyo, Japan, Tokyo, Japan, 7Institute of Liver Studies, King’s College Hospital, London, UK, London, UK, 8Department of Hepatology, University Hospitals Bristol NHS Foundation Trust, Bristol UK, Bristol, UK, 9Psychoncology Unit, Sant'Andrea Hospital - Faculty of Medicine and Psychology Sapienza University of Rome, Italy, Rome, Italy, 10AssistancePublique Hôpitaux de Paris, APHP, Hôpital Beaujon, Department of Radiology, Clichy, France ; Université Paris Diderot, Sorbonne Paris Cité, INSERM Centre de recherche Biomédicale Bichat Bea, Paris, France, 11Assistance Publique Hôpitaux de Paris, Hôpital Hôtel-Dieu, Centre d'Epidémiologie Clinique, France, Paris, France ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info The EORTC QLQ-HCC18 was administered with the core questionnaire, the EORTC QLQ-C30 to 272 patients from seven centres in six countries (Taiwan, Hong Kong-China, Japan, the United Kingdom, France, and Italy) in a crosscultural field validation study which was published elsewhere. We found Asian patients had significantly better quality of life scores than European patients in many subscales. Significantly better clinical characteristics were also found in Asian patients than European patients because of early detection of their illness. To clarify the effects of cultural vs. clinical characteristics, we conducted multi-way ANOVA, model selection, and correct multiple comparisons by using the method of false discovery rate. After adjusting for clinical characteristics, cultural effects existed in physical functioning, role functioning, and fatigue. In model selection, we found cultural effects existed in role functioning, insomnia, fatigue, and sexual interest. After correction for multiple comparisons, all cultural differences became non-significant. The difference in results may be due to differences in methods and criteria for significance. We concluded that both clinical characteristics affected the quality of life of patients with primary liver cancer. Early detection and prompt treatment by active surveillance of Asian countries may explain part of the results. Keywords: primary liver cancer, quality of life, the EORTC questionnaires, cultural differences 121/156 Hospital, Aarhus University Hospital, Aalborg, Denmark, 2Department of Epidemiology, School of Public Health, Aarhus University, Aarhus, Denmark Since August 15th 2007, it has been prohibited by law to smoke in public buildings, bars and restaurants in Denmark. It is expected that this will encourage smokers to stop smoking, dis-encourage youngsters to start smoking, and protect people from second-hand smoke. Many beneficial effects are expected by the introduction of this legislation. We will focus on the incidence of cardiovascular diseases in Denmark, since we expect smoking to have an acute effect on the incidence of cardiovascular diseases. Other studies (Sargent et al, 2004; Bartecchi et al, 2006) have shown up to a 40% reduction in heart attacks after six months. From the Danish national patient registry, we identified all incident cases from August 15th 2002 until August 15th 2011 of the following diseases: acute myocardial infarctions (N=74.861), stroke (N=125.815), venous thromboembolism (N=57.746). We used Poisson regression to estimate the effect of the smoke legislation law taking into account both trend and seasonal effects. Furthermore, we stratified by gender and age. P21.7 New tricks for an old dog: using the delta method for non-linear estimators, with P21.5 an application to competing risks in continuous time. Exploring the use of body mass index as a covariate in survival models of total Mark Clements, Robert Karlsson, Fredrik Wiklund, Henrik Grönberg knee replacement Karolinska Institutet, Stockholm, Sweden 1 1 2 David Culliford , Joe Maskell , Nigel Arden 1 University of Southampton, Southampton, UK, 2University of Oxford, Oxford, The delta method is an important tool in theoretical statistics, however computer-intensive methods are often preferred for variance estimation for UK non-linear estimators. This is partly due to linearity and normality assumptions Total knee replacement (TKR) is an effective intervention in patients with end- of the delta method not being satisfied, but also due to software stage knee osteoarthritis. The proportion of those undergoing TKR in the implementations being inflexible. This latter concern has been recently United Kingdom who are obese has been increasing recently. Time to event addressed, with implementations of the delta method using numerical partial models for knee replacement failure often incorporate body mass index (BMI) derivatives in SAS (PROC NLMIXED) and Stata (predictnl). We describe an R as an explanatory variable, but its use as a time-varying covariate, particularly implementation which has considerable flexibility. within large databases, has not been widely explored. As an application, we used data from a prostate cancer case-control study to We describe and compare a range of options for using BMI as a covariate in estimate the increase in cause-specific cumulative incidence (with competing survival analysis when the availability is variable and the timing irregular. This risks) of prostate cancer associated with a set of risk SNPs. Incidence rates situation is common within large, population-based general practice databases. were modelled using logistic regression, adjusting for population-level We use data from the UK General Practice Research Database (GPRD), incidence rates. Cumulative incidence to age 80 years was calculated in identifying all subjects (N=24738) undergoing TKR between 1991 and 2006, continuous time using an ordinary differential equation solver, adjusting for including all routinely recorded BMI values with measurement dates. Using Cox death due to other causes. Variance estimates for the log of cumulative regression, we model time to failure using different scenarios depending on the incidence were calculated using the delta method. For comparison, we timing of BMI (e.g. pre-TKR, post-TKR, multiply imputed annualised post-TKR), modelled incidence using Bayesian methods and re-calculated cumulative presenting hazard ratio (HR) estimates for BMI under different scenarios. For incidence. The confidence intervals using the delta method were similar to the example, standard Cox regression showed significantly higher risk of revision Bayesian credible bounds, where the width from the lower bound of the surgery for each unit of pre-operative BMI after adjusting for gender, age and confidence interval to the point estimate was 2% less wide and width from the number of comorbidities - HR 1.04 (CI 1.01,1.06, p=0.035). Furthermore, we upper bound to the point estimate was 3% wider. compare results using time-varying, post-operative BMI, with and without We found the delta method to be rapid and flexible. From the application, we multiple imputation. found that estimation of competing risks in continuous time is a feasible Although this work does not attempt to assess the theoretical validity of our alternative to non-parametric estimation. exploratory scenarios, it is hoped that developments may follow which make fuller use of certain types of repeated, irregularly observed covariates present P21.8 within medical research databases. Evaluation of the hierarchical power prior distribution Charlotte Rietbergen1, Ming-Hui Chen3, Irene Klugkist1 P21.6 1 Utrecht University, Utrecht, The Netherlands, 2UMC Utrecht, Utrecht, The Assessing the effect of smoking legislation on incidence of cardiovascular Netherlands, 3University of Connecticut, Storrs, Canada diseases Claus Dethlefsen1, Søren Lundbye-Christensen1, Anette Luther Christensen1, The power prior distribution in Bayesian statistics allows for the inclusion of data and results from previous studies into the analysis of new data. It enables Kim Overvad1,2 the researcher to control the influence of the historical data on the current data, 1 Department of Cardiology, Center for Cardiovascular Research, Aalborg by specifying a prior parameter that determines the amount of historical data to 122/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info be included. This parameter can either be a fixed user-specified value, or can be estimated from the data. For the latter, current literature states that the size of the weight parameter should depend on the commensurability of the historical and current study outcomes. In this research we question whether this is desirable, since differences between study results might be caused by sampling variability. Illustrated with a simulation study, we found on average only a marginal effect of sampling variability on the posterior estimate for the parameter of interest, at least for binomial data, and normal data with known variance. In some cases, however, the joint power prior provides posterior estimates farther from the true value than the estimates provided by a power prior with a fixed, self-chosen weight parameter. This supports our supposition that coincidental differences between current and historical data can affect the posterior distribution for the power parameter, and consequently the parameter of interest, especially for smaller samples. We advocate the inclusion of additional (expert) knowledge on the commensurability of the current and historical study populations, since only the commensurability of sample results can never fully justify the value of the weight parameter. over time, thus biasing estimates of time trends in disease incidence. This work aimed to (i) estimate the extent and pattern of false zeros and thereby estimate the true trend in disease incidence, and (ii) demonstrate the sensitivity of results to choice of modelling approach. Methods: Poisson models are commonly used for count data, although negative binomial models are better suited when there is general overdispersion. Excess zeros can be accounted for using ‘hurdle’ or ‘zero inflation’ versions of these models. Both approaches combine a Bernoulli process with the basic model; the Bernoulli probability itself can be made dependent on covariates. Random effects (RE) or GEE models can be used to allow for within-reporter correlation of counts. We hypothesised that a RE zero inflated negative binomial model, with time as a covariate in both the negative Binomial and the Bernoulli processes, was most appropriate for our data, but other models were fitted for comparison. Results: Estimates of the overall proportion of false zeros given by the various models will be presented and also the evidence for variation in the proportion over time. Estimates of time trends in true incidence with and without adjustment will also be presented. Software for these analyses will be discussed. P21.9 Exploring the estimator associated with the impact of a composite score of multiple binary exposures on continuous outcomes: An illustration using the Mediterranean Diet Score. Christina Bamia1, Marina Zangogianni2, Nikolas Pantazis1, Fotios Siannis3 1 University of Athens, Medical School, Dept. of Hygiene, Epidemiology & Medical Statistics, AthensS, Greece, 2National Hellenic Research Foundation, Athens, Greece, 3University of Athens, Dept. of Mathematics, Athens, Greece P21.11 Using the whole cohort when analyzing case-cohort data - some practical experiences Anders Gorst-Rasmussen1, Søren Lundbye-Christensen1, Anne Tjønneland2, Kim Overvad1 1 Center for Cardiovascular Research, Aalborg Hospital, Aarhus University Hospital, Aalborg, Denmark, 2Institute of Cancer Epidemiology, Danish Cancer Society, Copenhagen, Denmark In epidemiology it is often important to estimate the association of multiple correlated exposures with an endpoint of interest. An example is nutritional epidemiology where focus lies on the relation of nutrition (multiple exposures) with health. Evaluating this association through the coefficients estimated from regression models with all exposures included simultaneously presents difficulties in the interpretation and in evaluating complex interactions between the exposures. Using a composite score based on a function of the exposures is an alternative. The association of interest is then assessed through the estimator from regression models, of the relation of the composite score with the outcome. However, it is not clear what is measured by this estimator and how it relates to the regression coefficients associated with the individual exposures. We consider Xi, (i=1,..n) binary exposures (0,1) and a Z variable representing a composite score estimated as the summation of the n exposures with and without applying weights to each exposure. We first explore the properties of Z in relation to those of the Xi. Secondly, we consider linear regressions on a continuous outcome Y of Z, as well as, of Xi. We derive formulae for the regression coefficient b associated with Z and we show that this is a weighted average of the regression coefficients bi, associated with Xi. The weights are functions of the variances and the covariances of Xi. We illustrate the above with an example using Mediterranean Diet Score as composite score and Body Mass Index as the outcome of interest. The case-cohort design collects incident cases of a disease alongside a random sample of the full cohort, providing a cost-efficient way to study expensive biomarkers in large cohorts. In the Diet, Cancer & Health prospective cohort of 57,053 Danish participants, case-cohort designs are used extensively to investigate associations between adipose fatty acids and incidence of various diseases. Our in-house standard for statistical analysis is to use Horvitz-Thompson weighted Cox proportional hazards models with robust variance estimation. This model is easily understood and implemented by non-statisticians, but disregards valuable information about the exposuredisease association available from variables measured in the full cohort. Survey statistics provides a framework for incorporating such information in order to improve efficiency of estimators, and seems an interesting alternative to our current practice. However, a promise of theoretical superiority is but one aspect of a method in daily statistical practice. How much narrower confidence intervals can we really expect from survey estimators; are they simple enough to implement; and how do we communicate their rationale to clients? We report here a case study, from a consultant's perspective, of the performance and prospects of survey statistical methods in some typical case-cohort research problems encountered in the Diet, Cancer & Health cohort. P21.10 Removal of bias from incidence trend estimation using excess zero models Lesley-Anne Carter, Fiona Holland, Roseanne McNamee University of Manchester, Manchester, UK P21.12 Analysis methods comparison for censored paired survival data. A study based on survival data simulations with application on breast cancer. Alexia Savignoni1, Caroline Giard4, Pascale Tubert-Bitter2, Yann De Rycke1 1 Institut Curie, Hôpital, Paris, France, 2INSERM UMRS 1018, CESP, Villejuif, France, 3Université Paris Sud 11, Paris, France, 4Institut Curie, Hôpital René Huguenin, Saint Cloud, France Background: Extra zeros relative to the Poisson distribution are a common Censored paired survival data are natural (study on twins) or experimental are problem in longitudinal count data and, if not properly accounted for, could lead often described in clinical research analysis. Exposed-Non-Exposed studies to bias. In a disease surveillance system with monthly physician reporting, with patients paired on several prognostic factors are conducted to compare false zero reports were suspected. Furthermore, their extent might increase ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info the evolution between these two groups. Specific analysis methods have to be used with such correlated data. In our study we aim at evaluating the performances of four main methods using simulations: the Prentice Wilcoxon rank test, the frailty Cox model, the Wei Lin Weissfeld paired Cox model and the Holt paired stratified Cox model. The motivating application is a cohort study, which aims at the assessment of breast cancer prognosis related to a subsequent pregnancy occurring over time after treatment in young women. The exposed subject corresponds to a pregnant woman matched on several prognostic factors to a non-exposed one, i.e. a non-pregnant woman. Two thousand samples of pairs are realised. Each time to event, T1 and T2, are simulated according to an exponential survival model with its parameter equal to 0,01. Clayton and Marshall-Olkin copulas are used to model their correlation. Four parameters are chosen and will vary according to simulations scenarii the pair numbers, the percent of censure, the regression prognostic parameter and the Spearman correlation coefficient between T1 and T2. For a nominal 5% risk, power and error risk will be assessed. These four approaches are applied to real data of women cohort treated and followed in 8 different French hospitals for their breast cancer. P21.13 Identifying risk behavior for varicella infection using current status survival analysis Sandra Waaijenborg, Susan Hahné, Mirjam Knol, Liesbeth Mollema, Gaby Smits, Fiona van der Klis, Hester de Melker, Jacco Wallinga RIVM, Bilthoven, The Netherlands The median age of infection with the varicella zoster virus - a herpes-virus that can cause chickenpox in children and shingles in adults - is considerably lower in the Netherlands compared to other European countries. To gain insight in this deviation, we used the data form a large cross-sectional population-based serological study in which also data on contact patterns was collected. We aimed to study determinants of infection; with a special focus on contact pattern data. Due to the nature of the data collection, it could only be determined whether at the time of the study a person of a certain age had been infected with varicella. However, the exact age at infection remains unknown. Using current status survival analysis risk factors are identified. Our results show that contact patterns are clearly associated with the chance of infection. We noticed that young children who - on a more than average basis - attend daycare centers are at higher risk of being infected at a younger age. P21.14 123/156 latter condition, the magnitude of the confounding bias increased with increasing (i) prevalence of the confounder U, (ii) strength of its association with the outcome, and (iii) strength of the E1*E2 interaction for U. We also show that a variable that is not a confounder for the main effects of E1 and E2 on Y may still act as a confounder for E1*E2 interaction for Y. P21.15 Composite retrospective estimates of Drug Use Incidence from Periodic General Population Surveys in Spain. Albert Sanchez-Niubo1, Josep Fortiana2, Antònia Domingo-Salvany1 1 Drug Abuse Epidemiology, IMIM-Hospital del Mar, Barcelona, Spain, 2Dept. of Probability, Logic and Statistics. University of Barcelona, Barcelona, Spain In the United States in the nineties, due to a tradition of periodic General Population Surveys on Drug Use (GPS-DU), Gfroerer et al (Addiction 1992;87:1345-51) developed the retrospective method to estimate lifetime incidence of drug use. The method was little used as few countries kept long series of periodic GPS-DU and because of its poor reliability for less prevalent substances like heroin. Recently, other substances are taking the lead, with certain characteristics that allow reconsidering this method. The aim of our study was to adapt the retrospective method to exploit eight Spanish biannual GPS-DU (1995-2009) to estimate yearly lifetime incidence of cannabis and cocaine use and their age of onset cumulative incidence by birth cohort. Composite retrospective estimates were weighted means of the estimates of each survey. The weights consisted of estimates of variances within each survey. Five birth cohorts (1930-,1945-,1960-,1975-,1985-94) were considered. Yearly incidence estimates exhibit an increasing trend up to year 2000 (11.5 and 3.9 per 1,000 inhabitants aged 10-64 for cannabis and cocaine, respectively) and a smooth decrease since then (9.7 and 2.7 respectively in 2008). For both cannabis and cocaine in younger birth cohorts, cumulative incidences increased while age of onset decreased. Long series of periodic GPS-DU provide composite estimates which are more robust and have a wider coverage of retrospective ages of drug use onset. Despite inherent sources of bias from surveys on illicit substances, periodic GPS-DU can provide incidence measures, desirable to adequately plan, evaluate and improve prevention policies. P21.16 Piecewise linear Poisson regression models with unknown break-points Giota Touloumi, Nikos Pantazis, Evi Samoli Athens University Medical School, Dpt of Hygiene, Epidemiology & Medical Statistics, Athens, Greece Regression models with piecewise linear terms where the number and the position of the break-points are unknown are useful in many areas, like for When are interaction estimates confounded? example when studying time trends in cancer mortality. 1 1 2 Aihua Liu , Michal Abrahamowicz , Jack Siemiatycki We propose a maximum likelihood based methodology to fit such models. Data 1 McGill University, Montreal, Quebec, Canada, 2University of Montreal, are assumed to follow a Poisson distribution and all model parameters, Montreal, Quebec, Canada including the break-points can depend on other covariates. Transition functions Interaction analyses have become an integral part of modern epidemiology but are introduced in order to approximate the segmented relationship through a pose specific challenges. Observational studies of interactions may be affected continuous differentiable function and the break-points positions are reby unmeasured confounding but conditions under which an interaction effect parameterized in order to constraint them within a specific range. Maximization estimate will be biased remain unclear. We first investigate such conditions of the likelihood function is performed through numerical techniques. Variancethrough analytical derivations. Then, we report the results of simulations, which covariance matrix of the parameters can be obtained by the inverse of the allow us to quantify the impact of selected parameters on bias in point observed information matrix or by the outer product of gradients estimator. estimates and coverage rates of confidence intervals for the interaction effect. The performance of the proposed method is assessed through simulations We identify two different situations where the failure to adjust for the effect of a where besides the usual measures of bias and precision we estimate type I risk factor U, results in a biased estimate of the interaction between exposures and type II error rates when comparing nested models with different number of E1 and E2 on a binary outcome Y: (1) U is associated with E1 and has an break-points. The method is also applied to Greek cancer mortality data from interaction with E2 for Y; (2) the association between U and E1 varies 1968 to 2006 and compared to two alternative methods. depending on the value of E2. In simulations, where we assume U meets the 124/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info Simulation results showed that the proposed method is asymptotically unbiased with empirical coverage probabilities and type I error rates being very close to their nominal levels. Power to detect more complex trends was depending on the sample size, the difference between subsequent slopes and the position of the break-points. In general, the proposed method is flexible, expandable and easy to implement using modern optimization software. P21.17 A general conceptual and statistical framework for exposure-time-response relationships based on distributed lag non-linear models Antonio Gasparrini London School of Hygiene and Tropical Medicine, London, UK In biomedical research, a health effect is frequently the result of protracted exposures of varying intensity sustained in the past, a phenomenon sometimes described as an exposure-time-response association. This issue is common to various research fields, such as environmental, cancer or pharmacoepidemiology. The main complexity of modelling and interpreting such dependency lies in the additional time dimension needed to express the association, as the risk depends on both intensity and timing of past exposures. This contribution illustrates a general modelling framework for exposure-timeresponse associations based on the extension of distributed lag non-linear models (DLNMs), an approach originally developed in time series data. This modelling framework is based on the definition of a cross-basis, a bidimensional space of functions describing the dependency simultaneously in the spaces of the predictor and lag. A cross-basis is specified through a tensor product between two independent set of basis functions for the two dimensions, chosen among alternative options such as step, threshold or spline functions. The estimated association is represented as a risk surface for specific exposure and lag values, and predicted risk for specific exposure experiences can be computed. As an illustrative application, extended DLNMs are applied to investigate the relationship between occupational exposure to radon and lung cancer mortality, through Cox proportional hazard models. This flexible modelling framework, fully implemented in the R package dlnm, generalizes to various study designs and regression models, and can be applied to study the health effects of environmental factors, drugs intake or carcinogenic agents, among others. P21.18 On the validity of power simulation based on Fleishman distributions Jenö Reiczigel1, Tamás Ferenci2 1 Szent István University, Faculty of Veterinary Science, Budapest, Hungary, 2 Budapest University of Technology and Economics, Faculty of Electrical Engineering and Informatics, Budapest, Hungary Properties of statistical methods are often examined by simulation. Typical examples are checking robustness of a method against violation of its applicability conditions, or determining the power of a test under various distributional assumptions. For such simulations several distribution systems are available. One of them, the Fleishman system is fairly popular because of the computational ease and flexibility of the method. Fleishman distributions are defined as linear combinations of powers of standard normal variates. Mean, variance, skewness and kurtosis of a Fleishman distribution can be set to desired values. To examine the performance of a statistical test when applied to non-normal data, one can generate data from Fleishman distributions with given skewness and kurtosis, and apply the test to them. It is questionable however, whether these results are always valid. We found that for certain distributions (e.g. twocomponent normal mixtures), the fitted Fleishman distribution has a visibly different density function, despite having identical first four moments. The difference persists even if more moments are fitted. Since the performance of a statistical method may depend on the distribution of data even beyond the first few moments, simulation with Fleishman distributions may be misleading. We present examples with simulated power of nonparametric tests. Our conclusion is that if one wants to investigate the performance of a statistical method by simulation, it is better to generate distributions from observed data typical on that particular research field than to base the simulation on arbitrary distributions like the Fleishman system. P21.19 Prenatal, perinatal and neonatal risk factors for autism. A case-control study in Poland Dorota Mrozek-Budzyn, Agnieszka Kieltyka, Renata Majewska Jagiellonian University Medical College, Krakow, Malopolska, Poland The objective was to determine a relationship between pre-, peri-, and neonatal factors and autism. A case-control study was conducted among 288 children (96 cases with childhood or atypical autism and 192 controls individually matched to cases by the year of birth, sex, and general practitioners). Data on autism diagnosis and other medical conditions were acquired from physicians. All other information on potential autism risk factors were collected from mothers. The odds ratios for autism diagnosis were calculated using conditional logistic regression Autism risk was significantly higher when mothers were taking medications and smoked during pregnancy. It was also significantly associated with neonatal dyspnea and congenital anomalies. In gender analysis only congenital anomalies were significantly associated with autism for girls but all of mentioned factors stayed independent risk factors for boys. P22 Survival and multistate models P22.1 Bonferroni’s method to compare k survival curves with recurrent events Carlos Martinez1, Guillermo Ramirez2, Aleida Aular1 1 Carabobo University, Valencia, Carabobo, Venezuela, 2Central University of Venezuela, Caracas, Venezuela Survival analysis provides useful statistical tools to study events such as viral diseases, malignant tumors, machines failures, among others. This paper offers a statistical method of interest to epidemiologists, biostatisticians and other researchers interested in methodology to apply in medicine and other health research areas. Comparison nonparametric tests with recurrent events time data were proposed by Martinez et al. in 2009 and 2011. The main objective is to propose a new procedure based on Bonferroni's method for comparing survival curves of population groups with recurrent events. The idea is to apply a methodology with a sequential procedure on multiple contrasts with control type I error. The total tests number (q) increases if the total group number (k) build-up, where q=k(k-1)/2 tests.The hypothesis test is the following: H0: S1(t) = S2(t) = ... = Sk(t) H1: At least one Sr(t) is different with r=1,2,…,k. Sr(t) is the survival curve of the rth group and its estimation is made by the Generalized Product-Limit Estimator (GPLE or Kaplan-Meier estimator) proposed by Peña et al. in 2001. The survival functions will be estimated using R-language programs and counting processes. To illustrate Bonferroni's method for comparing survival curves, it will be used the database Byar, in which it was measured the time (months) of tumor recurrence in 116 patients with bladder cancer who were assigned randomly to the following treatments: placebo, pyridoxine and thiotepa. ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info 125/156 P22.2 space in at least one of the birth spacing (I st to Vth). However, the variables One-Sample Test for Goodness-of-Fit for Length-Biased Right-Censored varied from the first birth spacing to the fifth birth spacing. Breastfeeding is the only covariate which is noticed to be a significant protective factor associated Survival Data with each birth spacing. Furthermore, this study validates the developed Jaime Younger, Pierre-Jérôme Bergeron models with their prediction utilities for birth spacing. University of Ottawa, Ottawa, Ontario, Canada Cross-sectional surveys are often used in epidemiological studies to identify subjects with a disease. When estimating the survival function from onset of disease, this sampling mechanism introduces bias, which must be accounted for. If the onset times of the disease are assumed to be coming from a stationary Poisson process, this bias, which is caused by the sampling of prevalent rather than incident cases, is termed length-bias. A one-sample Kolomogorov-Smirnov type of goodness-of-fit test for right-censored lengthbiased data is proposed and investigated with Weibull, log- normal and loglogistic models. Algorithms detailing how to efficiently generate right- censored length-biased survival data of these parametric forms are given. Simulation is employed to assess the effects of sample size and censoring on the power of the test. The method is illustrated using length-biased survival data of patients with dementia from the Canadian Study of Health and Aging. P22.3 Planning and evaluating clinical trials with composite time-to-first-event endpoints in a competing risk framework Geraldine Rauch1, Jan Beyersmann2 1 Institute of Medical Biometry and Informatics, University of Heidelberg, Heidelberg, Germany, 2Institute of Medical Biometry and Medical Informatics, , University of Freiburg, Germany Composite endpoints combine several events of interest within a single variable. These are often time-to-first-event data which are analyzed via survival analysis techniques. To demonstrate the significance of an overall clinical benefit, it is sufficient to assess the test problem formulated for the composite. However, the effect observed for the composite does not necessarily reflect the effects for the components. Therefore, it would be desirable that the sample size for clinical trials using composite endpoints provides enough power not only to detect a clinically relevant superiority for the composite but also to address the components in an adequate way. The single components of a composite endpoint assessed as time-to-first-event define competing risks. We consider multiple test problems based on the cause-specific hazards of competing events to address the problem of analyzing both a composite endpoint and its components. Thereby, we use sequentially rejective test procedures to reduce the power loss to a minimum. We show how to calculate the sample size for the given multiple test problem using a simply applicable simulation tool in SAS . Our ideas are illustrated by a clinical study example. P22.5 Expanded renal transplant: A multi-state model approach Pablo Martínez-Camblor1, Jacobo de Uña-Álvarez2, Carmen Díaz Corte3 1 CAIBER, Oficina de Investigación Biosanitaria de Asturias, Oviedo, Asturies, Spain, 2Departamento de Estadística e IO, Universidad de Vigo, Vigo, Galicia, Spain, 3Hospital Universitario Central de Asturias, Oviedo, Asturies, Spain The statistical methods must be adapted in order to respond more sophisticate questions each time. In the analysis of time to event data, multistate models are a valuable tool which allows to face complex problems from a more realistic approach than the usual proportional hazard Cox models. They let us to take into account the different steps that the patients follow before the final resolution i.e. before reaching the main event in study. In addition, they show themselves flexible and they let us to avoid most of the usual conditions like, for instance, the proportional hazard assumption. In this work we are interested in the effects of the quality of the transplant organ in the final survival of the transplanted patients. In particular, we study the lifetime in people with kidney disease which after a dialysis period have been received a renal transplant. In order to increase the potential number of transplantation organs and reduce the waiting time, the usual quality standards for the transplanted kidneys are sometimes relaxed (the new criterion are labeled expanded criterion) and, these "expanded kidneys" are usually implanted in non suitable candidates (in the current data set, they were older with...). The organ failure and the effect of the bad renal function on other vital activities must be also considered. Results suggest that the expanded kidneys are only little worst than the usual ones. P22.6 Fine and Gray approach versus cause-specific hazards: competing models or just two views of the same story? Christine Eulenburg, Linn Woelber, Karl Wegscheider 2Department of Medical Biometry and Epidemiology, University Medical Centre Hamburg-Eppendorf, Hamburg-Eppendorf, Germany In a situation with competing risks, the cause-specific hazards model as well as the Fine and Gray model for the subdistribution hazard are established and recommended tools. While the cause-specific hazards model deals with instantaneous risks, the Fine and Gray method corresponds to marginal event probabilities. As the two models focus on different aspects of a multistate process, they are better understood as complements of one another than as substitutes. An application to a dataset of vulvar cancer patients being exposed to disease recurrence at different sites and to death demonstrates this. With P22.4 age, lymph node affection and depth of invasion the three most important Breast Feeding as a Time Varying - Time Dependent Factor for Birth Spacing: factors for recurrence-free survival are included as covariates. The joint Multivariate Models with Validations and Predictions interpretation of the differing results from the two approaches and moreover, of Rajvir Singh their differences enables a deeper insight into the process history than each model separately. HMC, Doha, Qatar Data used in the present study are from the National Family Health Survey (NFHS) (1992-93), India. The present study has developed Cox model analyses to see the effect of breastfeeding as a time varying and time dependent factor on birth spacing. While it is acknowledged that breastfeeding has a protected effect on birth spacing, such analysis of breastfeeding allows for a more nuanced understanding of the same. Multivariate analysis revealed breastfeeding, ever experience of fetal loss, education of women, employment status of women, education of husband, media exposure, survival status of index child and place of residence play an important part in extending birth P22.7 Autologous Stem Cell Transplant Study in Lymphoma Patients: Statistical Analysis of Multi-State Models Jana Furstova1,2, Zdenek Valenta3 1 Department of Mathematical Analysis and Application of Mathematics, Faculty of Science, Palacky University Olomouc, Olomouc, Czech Republic, 2First Faculty of Medicine, Charles University, Prague, Czech Republic, 3Department 126/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info of Medical Informatics, Institute of Computer Science of the ASCR, Prague, any subsequent complete cytogenetic remission at time t after the initiation of Czech Republic CML therapy. Survival analysis is a collection of statistical methods for inference on time-to- METHODS: Standard nonparametric statistical methods were used for event data. If several different events occur, specific methods have to be used estimating a principal characteristic of the current CML treatment: the in order to capture the complex structure of the data. A multi-state model is probability of being alive and leukaemia-free in time after CML therapy used for modeling a stochastic process which at any time occupies one out of a initiation, denoted as the current cumulative incidence of leukaemia-free discrete set of possible states (e.g. healthy, ill, dead). Transitions between the patients. states represent the events. The multi-state model is most commonly used in RESULTS: The results have shown a difference between the estimates of the the form of a Markov model, where the transitions follow a Markov process. current cumulative incidence function and the common cumulative incidence of Recent papers have shown that this approach is very flexible and offers broad leukaemia-free patients. Regarding the currently available follow-up period, the difference has reached the maximum (12.8%) at 3 years after the start of applications in biomedical research. The aim of this paper is to recall the Markov multi-state model and to apply the follow-up, i.e. after the CML therapy initiation. multi-state methods to a real data set obtained from an Autologous Stem Cell CONCLUSION: A new quantity for the evaluation of the efficacy of current CML Transplant Study in lymphoma patients carried out at the Clinic of Haemato- therapy that may be estimated with standard nonparametric methods has been oncology of the University Hospital in Olomouc (Czech Republic). States proposed in this work. It reliably illustrates a patient's disease status in time observed in this study are healthy (i.e. in remission), relapsed and dead. The because it accounts for the proportion of patients in the second and influence of several clinically relevant risk factors (especially quality of the subsequent disease remissions. Moreover, the underlying model is also transplanted graft) is analyzed with respect to their effect on the occurrence of applicable in the future, regardless of what the progress in the CML treatment the events of interest. The results obtained by means of multi-state models are will be and how many treatment options will be available, respectively. compared to those of standard survival analysis methods. The work on this project was partially supported by institutional grant RVO:67985807 and ESF P22.10 project CZ.1.07/2.4.00/174.0117. Imputing missing covariate values in presence of competing risk Matthieu Resche-Rigon, Sylvie Chevret P22.8 Université Paris diderot; UMR-S 717; Hôpital Saint-Louis, AP-HP, Paris, France Exploratory survival analysis using longitudinal mixed-models Due to its flexibility, its practicability and its efficiency compared to the complete Ian James, Elizabeth McKinnon case analysis, multiple imputation by chained equations is widely used to Murdoch University, Perth, WA, Australia impute missing data. Imputation models are built using regression models and In situations where interest focuses on time to achievement of an event which it is well known that to avoid bias in the analysis model, the imputation model is assessed for each individual at a series of discrete time points, one can must include all the analysis model variables including the outcome. estimate and compare approximate survival curves by formulating the data as In survival analyses, outcome is defined by a binary event indicator D and the a sequence of longitudinal binary outcomes, with pseudo-time points added observed event or censoring time T. Unfortunately, estimates obtained by direct post-event for comparison as necessary, and utilizing standard mixed model inclusion of D and T in the imputation model are biased. Using a Cox model, I. inference. Whilst this appears to be an inefficient approach, choice of a flexible White and P. Royston showed that the imputation model should include the form for the distribution function and reasonable autocorrelation provides a event indicator and the cumulative baseline hazard, and therefore useful and powerful general tool for exploratory survival analysis. It makes few recommended to include the Nelson-Aalen estimator distributional or structural assumptions, readily accommodates different In the competing-risks setting, subjects may experiment one out of K distinct censoring, observation and entry patterns and uses standard software. The and exclusive events. Two main approaches have been proposed. The most analyses can highlight where survival curves differ, can incorporate changing common approach models the cause-specific hazard of the event of interest covariate effects over time, particularly changing effects of baseline while the second approach models the sub-distribution hazard associated with differences, and can accommodate such things as multiple weighted the cumulative incidence. We propose to extend the work of I. White and P. assessments of occurrence of the event where this may be predicted with Royston to the competing-risks setting by including in the imputation model the uncertainty at each time point. We demonstrate the utility of the approach cumulative hazard of the competing events. Moreover, we will show that using flexible piecewise linear models, corresponding to piecewise constant cumulative hazards of all the events that compete to each other should be densities, based on both simulated data and in application to the analysis of included. times to event among HIV positive patients whose status is assessed at nonPerformance of our approach will be evaluated by a simulation study, then uniform visit times post-therapy. applied to a sample of 278 adult patients with acute myeloid leukaemia. P22.9 Estimation of current cumulative incidence of leukaemia-free patients in chronic myeloid leukaemia Tomas Pavlik1, Eva Janousova1, Ladislav Dusek1, Jiri Mayer1, Marek Trneny2 1 Masaryk University, Brno, Czech Republic, 2Institute of Haematology and Blood Transfusion, Prague, Czech Republic INTRODUCTION: Traditional measures of treatment efficacy such as cumulative incidence are unable to cope with multiple events in time, e.g. disease remissions or progressions, and as such are inappropriate for the efficacy assessment of the recent chronic myeloid leukaemia (CML) treatment. OBJECTIVES: To estimate the probability that a CML patient will be in first or in P22.11 Frequentist Evaluation of Bayesian Methods for Survival Data David Dejardin1, Emmanuel Lesaffre1,2 1 I-Biostat KULeuven, Leuven, Belgium, 2Department of Biostatistics, Erasmus MC, Rotterdam, The Netherlands, 3Global Biometric Sciences, Bristol-Myers Squibb, Braine l'Alleud, Belgium Bayesian methods are becoming increasingly popular, especially for the modelling of complex models, even to non-Bayesians. In addition, standard errors and confidence intervals (CIs) are easily obtained as a by-product from a Bayesian analysis. Although not explicitly stated, the posterior summary measures are often assumed to have frequentist properties. ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info 127/156 The coverage of a Bayesian 95% CI obtained with an objective prior has been studied by many. In some simple cases, correct coverage can be proven analytically. In other cases, their favourable properties have been shown by simulations (Agresti and Min, 2005). In this study, we explored the coverage properties of the CIs of several Bayesian survival approaches for the analysis of various types of censored data proposed by Ibrahim et al. (2001). The motivation for this study comes from a recently suggested non-Bayesian technique for the analysis of doubly-interval censored data and the difficulties we encountered in deriving CIs for the model parameters. The coverage and length of the Bayesian CIs were compared to their frequentist counter part for parametric and non-parametric approaches for right, interval and doubly-interval censored data. Our tentative conclusions are that the Bayesian approaches compare favourably to the frequentist approaches, although their properties are not uniformly better. The variety of approaches will also be illustrated on data from an ovarian cancer RCT. Aalen estimates in order to derive maximum-likelihood estimates of the regression coefficient. In a data example, we study the occurrence of bloodstream infections in neutropenic patients, where the competing risk is recovery. The cumulative proportional odds model revealed as possibly restrictive assumption an opposing effect on the categorical outcome variable, leading to rather extreme results with respect to the cumulative incidence functions. We also compared the relative merits of the proportional odds model with proportional cause-specific or proportional subdistribution hazards modelling. [1] Rajicic, N., Finkelstein, D.M., and Schoenfeld, D.A. (2009). Analysis of the relationship between longitudinal gene expressions and ordered categorical event data. Statistics in Medicine, 28(22):2817-2832. P22.13 The Cumulative Proportional Odds Model for Competing Risks Kristin Ohneberg, Jan Beyersmann, Martin Schumacher Institute of Medical Biometry and Medical Informatics, Freiburg, Germany P22.15 Modelling discharge from a neonatal unit: an application of competing risks. Sarah Seaton, Sally Hinchliffe, Paul Lambert, Bradley Manktelow Department of Health Sciences, University of Leicester, Leicester, UK Although the proportional odds model has been used within many different fields of research, it rarely has been used for analyzing competing risks data. Rajicic, Finkelstein and Schoenfeld [1] proposed a test for ordered categorical event data using the cumulative proportional odds model and derived a onedegree of freedom hypothesis test for detecting an opposing effect of a predictor variable on the two extreme outcome categories. Instead of hypothesis testing this talk focuses on parameter estimation within the proportional odds model in order to compute model-based cumulative incidence functions. The approach presented in this talk uses stratified Nelson- Estimates of length of stay for babies in acute neonatal care are vital for clinical decision making, counselling parents and planning services. Previous work has predominately focused only on the length of stay of babies who survive to discharge, removing those babies who die on the neonatal unit from the analysis. However, it is highly likely that the babies that die have underlying conditions that make them fundamentally different to those that survive to discharge. Competing risks methodology allows estimation of the probability of discharge whilst accounting for the competing risk of death. We used data from The P22.14 Flexible modeling in Relative Survival: additive vs multiplicative model Amel Mahboubi1, Laurent Remontet2, Christine Binquet1, Jonathan Cottenet1, Michal Abrahamowicz3, Catherine Quantin1 P22.12 1 Evaluation of estimation methods and tests of covariates in repeated time to Département de l’information médicale, CHU Dijon, INSERM U866, Dijon, France, 2Hospices Civils de Lyon, Lyon, France, 3McGill University, Montreal, event parametric models Canada 1 2 1 Marie Vigan , Jérôme Stirnemann , France Mentré 1 INSERM, UMR 738, Univ Paris Diderot, Sorbonne Paris Cité, Paris, France, Accurate assessment of the effects of continuous prognostic factors requires 2 flexible modeling of both time-dependent (TD) and non-linear (NL) effects [1]. Hospital Jean-Verdier, Univ Paris XIII, Bondy, France To address this complex issue, in Relative Survival (RS), two flexible The analysis of repeated time to event (RTTE) data requires frailty models and extensions of the seminal Estèveet al model [2] have been developed. These specific estimation algorithms. The aims of this simulation study were 1) to extensions differ in that the TD and NL effects of the covariate on the log assess the estimation performance of the Stochastic Approximation excess hazard are assumed to be (i) additive in [3] but (ii) multiplicative in [4]. Expectation Maximization (SAEM) algorithm in MONOLIX and the Adaptative Specifically, the disease-specific hazard are written, respectively as: Gaussian Quadrature in PROC NLMIXED of SAS, 2) to evaluate the properties lc(t|z)=exp(g(t))*exp(ai(zi)+bi(t)*zi) andlc(t|z)=exp(g(t))*exp(ai(zi)*bi(t)) of the tests of a dichotomous covariate on the occurrence of events. where: g(t) represents the baseline log hazard and, for a continuous covariate The simulation settings are inspired from a real clinical study. We assumed an z : a (z ) and bi(t) represent, respectively, its NL and TD effects. exponential model with random effect and covariate effect additive on log i i i lambda. We simulated 500 datasets with 200 subjects. We defined the fixed We carried out a systematic comparison of theadditive vs multiplicative RS effect lambda=0.002 month-1, its variance omega2=1 and a maximum follow-up models. The two models were applied to both simulated datasets (generated of 144 months. Various values for the effect of the covariate were studied and assuming different relationships between the two effects) and real-life datasets we also varied omega and the number of subjects. We evaluated estimation (where the relationshipsare unknown). performance through the relative bias and the relative root mean square error Results of the two models will be evaluated and compared, in terms of the (RMSE). We studied the properties of both the Wald and the likelihood ratio shape of estimated TD and NL effects, their significance, models' fit to real-life test (LRT). Estimations were performed with SAS v.9.3 (with 5 quadrature data, and ability to identify the ‘true' model (in simulations). points) and MONOLIX v.4.0 (with 3 Markov chains). The two algorithms showed similar estimation performances with small biases [1] Abrahamowicz& McKenzie, Stat Med2007;26:392-408 and RMSE, which decrease as the number of subject increases. Type I errors [2] Estève et al, StatMed1990;9:529-538 were closed to 5% and power varied as expected. Despite the small number of repeated events, both algorithms provided good [3] Remontetetal, StatMed2007;26:2214-2228 [4] Mahboubietal, StatMed2011;30:1351-1365. estimates of the parameters and tests with adequate properties. 128/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info Neonatal Survey (TNS), which collects data on babies admitted to 29 neonatal units in the East Midlands and Yorkshire regions of England. As there was a non-proportional effect of gender in the study we chose to model the causespecific hazards for death and discharge using a flexible parametric survival model as it can easily incorporate time-dependent effects. The cumulative incidence function was then estimated for both death and discharge through a transformation of the cause-specific hazards using numerical integration. 2176 babies were identified from TNS as being born at 24-28 weeks gestational age. As gestational age increased the probability of survival to discharge increased. For both male and female babies born at 24 weeks gestation with a birthweight equal to the tenth centile, approximately 60% had died within the first 40 days of life. The analysis has produced a useful tool for clinicians to plan neonatal services and counsel parents. dependent competing risk of treatment can be eliminated and where we want to estimate the marginal survival distribution of natural conception. Without assumptions on the correlation between the two competing events, this marginal distribution is not identifiable. After determining the upper and lower bounds of the marginal distribution, we use inverse probability of censoring weighting techniques to correct for the dependent competing risk using various patient characteristics measured during the diagnostic workup. To account for the possibility of residual dependence conditional on the measured covariates, we perform a sensitivity analysis modelling the assumed negative dependence between the two competing events in a multivariable survival model with a copula. Results will be illustrated using a dataset of 5630 couples with unfulfilled child wish. The three year pregnancy rate in this cohort is 41% when assuming treatment censoring is independent of natural conception, whereas assuming natural conception chances were zero after treatment start leads to an estimate of only 22%. P22.16 Using restricted cubic splines to approximate complex hazard functions in the P22.18 analysis of time-to-event data Models for the Subdistribution Hazard of a Competing Risk under Left Mark Rutherford, Michael Crowther, Paul Lambert Truncation - a Comparison of two Approaches University of Leicester, Leicester, UK Michael Lauseker1, Andrea Kuendgen2 If interest lies in reporting absolute measures of risk from time-to-event data 1Institut für medizinische Informationsverarbeitung, Biometrie und then obtaining an appropriate approximation to the shape of the underlying Epidemiologie, Ludwigs-Maximilians-Universität, München, Germany, 2Klinik für hazard function is vital. Real-life hazard functions can be complex with, for Hämatologie, Onkologie und Klinische Immunologie, Heinrich-Heineexample, multiple turning points and standard parametric models may fit Universität, Düsseldorf, Germany poorly. It has previously been shown that restricted cubic splines can be used to approximate complex hazard functions in the context of time-to-event data. When analyzing the subdistribution hazard of a competing risk, the Fine and The degree of complexity for the spline functions is dictated by the number of Gray model is usually applied. But when left truncation is present, this model knots used to model the hazard function. Through the use of simulation, we does no longer work. The problem was recently solved by Geskus (Biometrics, show that provided a sufficient number of knots are used, the approximated 2011) as well as by Zhang, Zhang and Fine (Stat. Med., 2011) proposing hazard functions given by restricted cubic splines fit closely to the true function different weights. for a range of complex hazard shapes. The objective was to compare both approaches in the case of left truncation The simulation study is motivated by a dataset of breast cancer patients in depending on one covariate. England and Wales, which has a hazard function with two turning points. In Similar to the setting that Fine and Gray had used, we performed a simulation the simulation, complex hazard shapes were generated using a two- experiment for the situation described above, varying the sample size, the component mixture Weibull distribution. The flexible parametric modelling balance of the competing events and the regression coefficients. For the approach was fitted to the simulated data using a range of values for the estimator of Zhang et al. we used the variant with the stratified nonparametric degrees of freedom. The fit of the functions is assessed by using area weights. differences between the true and fitted functions. Selection criteria (AIC and We evaluated both approaches on a real data example concerning the BIC) are also compared to assess whether they select the degrees of freedom competing events „disease progression" and „death without prior progression" for the "best-fitting" model. The results show that provided appropriate care is in patients with myelodysplastic syndromes, where two different treatments taken, restricted cubic splines provide a good approximation to complex hazard were compared. In this particular data set, only patients from one treatment functions. group were left truncated, while patients from the other one were observed without delay. Both approaches seem to be equal when weights do not depend on covariates. P22.17 When weights depend on one covariate we found visible differences in the Correcting for a dependent competing risk in the estimation of natural estimated coefficients of that variable, but only slight differences in the conception chances coefficients of the others. So in the case of left truncation depending on one covariate, Zhang's approach should be preferred. The choice of weights did not Nan van Geloven1, Ronald Geskus2, Ben Willem Mol3, Koos Zwinderman2 1 Clinical Research Unit, Academic Medical Centre, University of Amsterdam, have any influence on the standard error of the regression coefficients. Amsterdam, The Netherlands, 2Department of Clinical Epidemiology, Biostatistics and Bio-informatics, Academic Medical Centre, University of P22.19 Amsterdam, Amsterdam, The Netherlands, 3Centre for Reproductive Medicine, Predictions and life expectancies in Illness-death model Department of Obstetrics and Gynaecology, Academic Medical Centre, Pierre Joly, Célia Touraine, Mélanie Le Goff University of Amsterdam, Amsterdam, The Netherlands When estimating the probability of natural conception from observational data 1) INSERM, ISPED, INSERM U897, Bordeaux, France, 2) Univ. Bordeaux, on couples with an unfulfilled child wish, the start of assisted reproductive ISPED, INSERM U897, Bordeaux, France therapy (ART) is a competing event that cannot be assumed to be independent of natural conception. In clinical practice, interest lies in the probability of natural conception in the absence of ART, as this probability determines the need for therapy. We are thus faced with a situation where for new patients the In longitudinal studies, several events can occur on the same individual. One approach to model such data is the multi-state model, which allows subjects to move over time within a finite number of states. We will focus our attention in this work to the illness-death model, which is widely used in the medical ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info 129/156 literature. In this model, individuals are allowed to move from the "health" state to the "illness" state or the "death" state and from the "illness" state to the "death" state. In this model, we can model the effects of covariates on the three transition intensities. The estimated regression parameters are useful to determine risk factors involved in each transition. Transition intensities can also be estimated and for example, these estimations can be useful to compare the rate of death among diseased and non-diseased subjects. However, we may estimate other quantities which provide additional information, sometimes more relevant for clinicians or epidemiologists. For example, we can give life expectancies and transition probabilities for given value of covariates. We can also give probabilities for the status of subjects for a given time window, for example five years after a visit, in order to find prognostic factors. The aim of this work is to show how to estimate these quantities using the estimated regression parameters and the estimated transition intensities. As an illustration, we give some examples of predictions using data from a large cohort study on cognitive aging (age and sex-specific life expectancies without dementia,... ). With linked register and cause of death data becoming more accessible than ever, competing risks methodology is being increasingly used as a way of obtaining "real world"' probabilities of death broken down by specific causes. As this type of analysis relies on the use of cause of death data, it is important, in terms of the validity of these studies, to have accurate cause of death information. However, it is well documented that cause of death information taken from death certificates is often lacking in accuracy and completeness. We assess through use of a simulation study the effect of under and overreporting of cancer on death certificates in a competing risks analysis consisting of three competing causes of death: cancer, heart disease and other causes. Using realistic levels of misclassification, we consider 24 scenarios. The bias was highest in the older age groups on both the absolute (cumulative incidence function) and relative scale (cause-specific hazard ratio). Considering that misclassification is most likely to occur in these age groups, caution should be taken when making conclusive remarks about the probability of death from different causes. In the younger age groups, however, the bias resulting from misclassification of cause of death was reasonably small. P22.20 Estimation of avoidable deaths based on the theory of competing risks Arun Pokhrel Finnish Cancer Registry, Helsinki, Finland P22.22 Modelling and utilising the baseline hazard in prediction models of clinical outcomes: a missed opportunity Kym Snell1, Deborah Stocken2, Lucinda Billingham1, Thomas Debray3, Karel Moons3, Richard Riley1 1 MRC Midland Hub for Trials Methodology Research, University of Birmingham, Birmingham, UK, 2Cancer Research UK Clinical Trials Unit, University of Birmingham, Birmingham, UK, 3Julius Centre for Health Sciences and Primary Care, Utrecht University, Utrecht, The Netherlands Background/objective The concept of 'avoidable deaths' has been of growing interest in populationbased cancer survival studies. In a recent study (Pokhrel et al. 2010), the theory of competing risks by Chiang (1968) was used to estimate avoidable deaths. The aim of this study is to present how the Chiang's method can be used to estimate the number of avoidable deaths. Material Patients diagnosed in Finland with cancer at 27 sites in 1971-2005 were linked with population censuses made every five years in 1970-2000 to obtain patient's educational level. Results By assuming the cancer mortality of high education group (13 years of education or more) for all, 6% of the cancer deaths in patients diagnosed at ages 25-89 years during first five years after diagnosis in 1971-1985 would be theoretically avoidable. For periods 1986-1995 and 1996-2005, these proportions were even higher, 7 and 9% respectively. Conclusion The crude death probabilities derived using Chiang’s method can be used to estimate the avoidable deaths by eliminating the mortality differences between different groups of cancer patients. Deaths saved from one cause will not be actually saved because of competing risk mortality due to other causes. As the deaths will not be saved for a long time, person-years savings are more important. References: Chiang CL. Introduction to Stochastic Processes in Biostatistics 1968; Wiley: New York. Pokhrel A, Martikainen P, Pukkala E, Rautalahti M, Seppä K and Hakulinen T. Education, survival and avoidable deaths in cancer patients in Finland. Br J of Cancer 2010: 1109-1114. In medical research, there is a major interest in developing statistical models that predict the risk of a future clinical outcome for individuals. Such prediction models utilise multiple prognostic factors, and aim to predict risk for individuals based on their own characteristics, to inform therapeutic decisions and patient counselling. When prediction models are developed using time-to-event data, one approach is to use Cox regression. This produces a risk score for individuals, based on the parameters in the model. However, this score is difficult to interpret directly. Clinically, it is more informative to know individual risk probabilities over time, but Cox regression does not provide this as the baseline hazard is not estimated. Alternative approaches, such as flexible parametric modelling overcome this issue. In this talk, we present a systematic review of prediction models published in the general medical literature since 2006, and describe how researchers assess and utilise the baseline hazard. Our findings reveal that the majority of articles use a Cox regression model and ignore the baseline hazard, with the model's risk score often simply categorised to define risk groups whose average prognosis differs over time. Using a dataset in pancreatic cancer, we show why this is a wasted opportunity and potentially misleading, as an individual's risk may vary considerably from their group's average. We show that approaches such as the Royston-Parmar model, which flexibly models the baseline hazard using cubic splines, should rather be used to enable individual survival probabilities to be predicted. P22.23 Explaining differences in post-transplant survival between two studies in chronic myeloid leukaemia through identification of predictive factors by a Cox proportional hazard cure model Markus Pfirrmann1, Ruediger Hehlmann2 P22.21 1 Ludwig-Maximilians-Universitaet Muenchen, Insitut für Medidizinische The Impact of Under and Over-recording of Cancer on Death Certificates in a Informationsverarbeitung, Biometrie und Epidemiologie (IBE), Muenchen, Competing Risks Analysis: A Simulation Study Germany, 2Medizinische Fakultaet Mannheim der Universitaet Heidelberg, Sally R. Hinchliffe, Keith R. Abrams, Paul C. Lambert Mannheim, Germany University of Leicester, Leicester, Leicestershire, UK 130/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info Background: In two consecutive German studies on chronic myeloid leukaemia, patients were randomised to receive haematopoetic stem cell transplantation (HSCT) with a related donor. Despite supposedly comparable transplantation procedures, the survival probabilities after transplantation in first chronic phase of the patients treated in study III were significantly lower than the probabilities of the patients in study IIIA. Objective: To investigate whether predictive factors for post-transplant survival can explain the outcome differences. Methods: For the identification of predictive factors, the Cox proportional hazard cure model was used [Sy and Tylor, Biometrics 2000]. Parameter estimation was performed by application of the SAS macro of Corbière and Joly [Comput Meth Prog Bio, 2007]. Results: Donor matching, age, and time of HSCT after diagnosis were identified as predictive factors. In an independent dataset of transplantation registries, the influence of calendar time was determined. Including the predictive variables as a combined risk score in the cure model, the four resulting risk groups had a significant influence on latency (survival of the uncured patients) as well as on the incidence of cure. Added as a further factor, study origin had no significant impact any longer. Conclusions: Differences in influential predictive factors contributed to significantly different post-transplant survival probabilities between the studies. Apart from the explained variation in survival in dependence on the predictive risk factors, also random variation might have a share in the outcome discrepancy. P22.24 The Use of Latent Trajectories in Survival Models to Explore the Effect of Longitudinal Data on Mortality Mathieu Bastard1, Jean-François Etard2 1 Epicentre, Paris, France, 2UMI 233 TransVIHMI, Institut de Recherche pour le Développement, Université Montpellier 1, Montpellier, France Lucie Biard, Laurence Desjardins, Sophie Piperno-Neumann, Pascale Mariani, Corine Plancher, Yann De Rycke Institut Curie, Paris, France Therapeutic advances allow for prolonged disease-free survival and sometimes even cure of certain malignancies. In these cases, standard survival analysis methods, such as Cox proportional hazards model, with or without timedependent covariates, may not be applicable. They will not adequately account for the effect of covariates on the event. Cure models, either parametric or semiparametric, have been specifically developed to account for a cured fraction in a studied population. We performed a prognostic analysis of survival of uveal melanoma patients treated by first-line enucleation, at the Curie Institute, Paris, between 1982 and 2009. We studied specific metastasis-free survival to allow for a cure rate in the sample. We implemented a so-called biological parametric cure model, developed by AY Yakovlev at the Curie Institute, on the basis of tumor latency. We performed univariate and multivariate analyses. The effect of covariates on the cure rate is modelized by a logistic function while the specific survival of the uncured fraction follows a log-logistic function, under the proportional hazards assumption for the effect of covariates on the time to event. Procedure of analysis with the cure model addresses the specific issues of a cure-rate situation: numerous statistical hypotheses are tested (palliative and/or curative effect of a covariate). The model provides estimates of cure rates, depending on covariates (OR), as well as estimates of the probability of metastasis-free survival of the uncured patients (HR), characterizing the distinct prognostic effects of covariates. P22.26 Performance of parametric survival models under non-random interval censoring: a simulation study Nikos Pantazis1, Michael G Kenward2, Giota Touloumi1, on behalf of CASCADE 0 In survival models, time-dependant covariates increase the complexity of both Collaboration in EuroCoord 1 methodology and interpretation of results. Using latent trajectories is an Athens University Medical School, Dpt of Hygiene, Epidemiology & Medical alternative method to take into account time-dependant data in survival models Statistics, Athens, Greece, 2Medical Statistics Department, London School of which leads to an easier model building and interpretation of results. Hygiene and Tropical Medicine, London, UK We describe the different steps of the method and we apply it to explore the In many medical studies, individuals are seen at a set of pre-scheduled visits. effect of adherence to antiretroviral treatment (ART) and CD4 cell-count over In such cases, when the outcome of interest is the occurrence of an event, the time on mortality in HIV-infected patients. corresponding times are only known to fall within an interval, formed by two First, we identify the latent trajectories using a generalized linear latent and consecutive visits. These data are called interval-censored. All available mixed model. Then, we assign to each patient a unique latent trajectory based methods for the analysis of interval-censored event times rely on a simplified on the maximal membership probability. We thus obtain a new discrete variable likelihood function assuming that the only information provided by the which summarizes the whole trajectory of longitudinal data for a given patient. censoring intervals is that they contain the actual event time (i.e. nonFinally, we include this new variable as covariate in a classical survival model informative censoring). to estimate its effect on mortality. In this simulation study we assess the performance of parametric models for In a first example, we identify three latent trajectories of adherence and we interval-censored data when individuals miss some of their pre-scheduled visits show that compared to patients with a low adherence trajectory, patients with completely at random (MCAR), at random (MAR) or not at random (MNAR) intermediate and high adherence trajectory are less at risk to die (Hazard Ratio while making a comparison with a simpler approach often used in practice. A (HR) 95% CI: 0.38 (0.21;0.69) and 0.12 (0.04;0.34), respectively). In a second sample of HIV-1 infected individuals from the CASCADE study is used for example, two latent trajectories of CD4 are identified, and patients with high illustration in an analysis of the time between antiretroviral treatment’s initiation and viral load suppression. CD4 trajectory were less at risk to die on ART (HR: 0.19 (0.08;0.47)). This method should be considered with interest in clinical and observational Results suggest that parametric models based on flexible distributions (e.g. research as it provides an easy way to explore and interpret the link between generalised Gamma) can fit such data reasonably well and are robust to MCAR or MAR mechanisms. Violating the non-informative censoring longitudinal data and mortality or other time-to-event outcomes. assumption though, leads to biased estimators with the direction and the magnitude of the bias depending on the direction and the strength of the P22.25 MNAR mechanism. Simplifying the data, assuming that event times coincide Telling curative from palliative effects of covariates in prognostic analysis in a with the end of the interval, and applying standard survival analysis techniques, population with a cured fraction: Application of a biological cure model to can yield misleading results even when missingness depends only on a metastasis-free survival in uveal melanoma patients baseline covariate. ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info P22.27 The illness-death model to study progression of chronic kidney disease Julie Boucquemont1, Marie Metzger2, Georg Heinze3, Karen Leffondré1 1 Univ. Bordeaux, ISPED, Centre INSERM U897-Epidemiologie-Biostatistique, Bordeaux, France, 2Inserm U1018 Centre for Research in Epidemiology and Population Health, Univ. Paris Sud 11, Villejuif, France, 3Medical University of Vienna, Center for Medical Statistics, Informatics and Intelligent Systems, Vienna, Austria 131/156 software/pshreg/, by means of analysis of our motivating example. P22.29 Multistate modeling in the analysis of cost-effectiveness of NSCLC treatments Mathilda Bongers1, Veerle Coupe1, Cary Oberije2, Carin Uyl-de Groot3, Dirk de Ruysscher2 1 Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, The Netherlands, 2Maastricht University Medical Centre, Department of Chronic kidney disease (CKD) progression can be defined by the occurrence of Radiotherapy, MAASTRO Clinic, Maastricht, The Netherlands, 3Institute for different types of events such as progression in CKD markers or "hard" Medical Technology Assessment, Erasmus University Medical Center, outcomes like dialysis. Death is a competing event for all these events of Rotterdam, The Netherlands interest. While the time-to-event is exactly known for dialysis, it is systematically interval-censored for progression defined by repeated measures Traditionally, the measurement of effectiveness as part of a cost-effectiveness of quantitative CKD markers. This interval-censoring produces uncertainty on analysis of cancer treatments has involved estimation of mean or median the disease stage just before death, for subjects who die during follow-up. survival based on one or more studies with a single endpoint only. To establish Such uncertainty can be accounted for using an illness-death model (IDM) for the cost-effectiveness of new, individualized treatment strategies in cancer, interval-censored data. In a simulation study, we have shown that this more advanced tools for the estimation of effectiveness are required because approach conducts to less biased estimates of the effects on the risk of ‘illness' clinical and molecular characteristics need to be taken into account. of risk factors that also affect death, as compared to standard cause-specific In addition, time to and occurrence of intermediate events such as tumor hazards models. However, to our knowledge, the IDM for interval-censored recurrence and metastasis are important to establish the main outcomes in data has never been used to investigate CKD progression. cost-effectiveness studies, namely costs and quality of life. In this study we The objective of this study is to elucidate when the IDM should be considered introduce multistate modeling to obtain the transition probabilities in a Markov for estimating the effect of risk factors on CKD progression. To this aim, we model , for different patient profiles. The aim of the model is to evaluate the compare the estimates from the IDM and standard cause-specific and costs and effects of a new treatment strategy in radiotherapy compared to care. subdistribution hazards models, for selected risk factors of CKD progression, current using cohort data. Different events of interest, defined by quantitative CKD We used data from the Maastro Clinic in Maastricht, The Netherlands. The markers or dialysis, are considered. Our results enable us to distinguish dataset included 322 patients with data on patient- and tumor characteristics, practical situations where all estimates are similar from those where they and the time of occurrence of local recurrence, distant metastases and death. substantially differ. These results, combined with our simulation results, All patients received radiotherapy with curative intent, a subgroup of patients conduct to some recommendations on when the IDM should be used for was included in a dose-boosting study. Using multi-state regression models, we developed a micro-simulation Markov model consisting of 6 health states, investigating CKD progression. which are: treatment (initial state), local recurrence, distant metastases, local recurrence and distant metastases, and death. The R package version 2.11.0 P22.28 and the mstate package version 2.6.0 were used for the multistate modeling. Proportional and non-proportional subdistribution hazards regression with SAS Maria Kohl1, Karen Leffondré2, Georg Heinze1 P22.30 1 Medical University of Vienna, Vienna, Austria, 2Université Bordeaux 2, ISPED, Computationally simple estimation and improved efficiency for special cases of Bordeaux, France double truncation We consider a study on determinants of progression of chronic kidney disease, Rebecca Betensky1, Matthew Austin1, David Simon2 where the outcome is time to dialysis, with death as competing event. Some of 1Harvard School of Public Health, Boston, MA, USA, 2Beth Israel Deaconess the risk factors show time-dependent effects on the subdistribution hazard Medical Center, Boston, MA, USA causing misspecification of a proportional subdistribution hazards (PSH) regression model. We present a new SAS macro %PSHREG that can be used Doubly truncated survival data arise when event times are observed only if to fit a PSH model but also accommodates the possibility of non-PSH. Our they occur within subject specific intervals of times. Existing iterative estimation macro first modifies the input data set appropriately and then applies SAS's procedures for doubly truncated data are computationally intensive (Turnbull, standard Cox regression procedure, PROC PHREG, using weights and 1976; Efron & Petrosian, 1999; Shen, 2008). These procedures assume that counting-process format. With the modified data set, standard methods can the event time is independent of the truncation times in the sample space that then be used to estimate cumulative incidence functions for an event of conforms to their requisite ordering. This type of independence is referred to as interest. In general, proportional cause-specific hazards do not ensure PSH. In quasi-independence. We identify and consider two special cases of quasicase of non-PSH, random censoring usually distorts the estimate of the time- independence: complete quasi-independence and complete truncation averaged subdistribution hazard ratio of a misspecified PSH model, as later dependence. For the case of complete quasi-independence, we derive the event times are underrepresented due to earlier censoring. To address this nonparametric maximum likelihood estimator in closed-form. For the case of issue, we can optionally weight the summands of the estimating equations, i.e., complete truncation dependence, we derive a closed-form nonparametric the risk sets at each event time, by inverse-probability-of-censoring or by estimator that requires some external information, and a semi-parametric number-at-risk expected had censoring not occurred. While the former weights maximum likelihood estimator that achieves improved efficiency relative to the make time-averaged effect estimates independent from the observed follow-up standard nonparametric maximum likelihood estimator, in the absence of distribution, the latter allow an appealing interpretation of the average external information. We demonstrate the consistency and improved efficiency subdistribution hazard ratio as ‚odds of concordance‘ of time-to-dialysis with of the estimators in simulation studies and through asymptotic derivations, and the risk factor. We illustrate application of these extended methods for illustrate their use in application to studies of AIDS incubation and Parkinson’s competing risks regression using our macro, which is freely available at disease age of onset. P22.31 http://cemsiis.meduniwien.ac.at/en/kb/science-research/software/statistical- 132/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info Sample size calculation for time-to-event outcomes in randomized controlled trials: Aan evaluation of standard methods Mike Bradburn1, Ly-Mee Yu2 1 University of Sheffield, Sheffield, UK, 2University of Oxford, Oxford, UK Background Sample size calculations for time-to-event outcomes are usually based on the number of events needed to detect a postulated hazard ratio (HR). The derivation of this HR often incorporates data from previous studies. Although this may be a previously reported HR (e.g. from a Cox regression model), it may equally be derived from either the survival proportions at a fixed point in time or from the ratio of the median survival times. Objective This work is intended to review the performance of different estimators of the HR, and to serve as a guide (and a caution) to practising statisticians when undertaking sample size calculations. Method This evaluation is primarily simulation based, comparing how estimators perform in different scenario including survival distribution (including exponential, Weibull, and non-PH), type of censoring, and the extent of small degree of departures from proportionality in hazards. Results Unsurprisingly, the HR (and hence sample size) is highly inaccurate when evaluated on small samples, or evaluated from the ratio of survival probabilities early in the study duration. The overall model-based estimator of the HR is the preferred method. Discussion Although performance of the methods varies by scenario, we advocate deriving an overall measure of the HR; methods proposed by Parmar (Statistics in Medicine, 1998) can be used to derive these. We also propose a minimum of 100 events per arm are needed if results from previous studies are used to calculate a plausible hazard ratio. ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info 133/156 Author's Index Aalen, Odd O C32.2 Aris, Emmanuel C5.1 Abellan, Juan José C12.3 Ariti, Cono C34.4 Abisheganaden, John C11.4 Armstrong, Ben C34.4 Abrahamowicz, Michal C7.1, P21.14, P22.14 Armstrong, Bruce C23.5 Abrams, Keith C16.2, P12.4, P15.6, P22.21 Arnould, Benoit P6.2 Abu-Assi, Emad C28.2 Ashby, Deborah IP3, C11.2 Adolf, Daniela C13.1 Augard, Christele P11.2 Agris, Jacob P4.26 Augustyniak, Malgorzata P14.8 Agris, Julie P4.26 Aular, Aleida P22.1 Aguirre, Urko P20.19, P20.24 Austin, Matthew P22.30 Ahmed, Ikhlaaq P20.17 Aydemir, Aida P4.26 Ahmed, Ismaïl I6.2 Ayerbe Garcia-Mozon, Luis P13.3 Ahmed, Roman P11.5 Ayis, Salma P13.3, P16.2 A.K., Mathai P4.7 Baart, Mireille P20.5 Akram, Muhammad C14.3, P4.28 Bailey, Michael C14.3 Alavi Majd, Hamid P14.7 Bakke, Øyvind C21.1 Al-Kadhimi, Gillian P21.4 Bakoyannis, Giorgos C26.4 Allignol, Arthur C32.5 Bakshi, Andisheh C30.1 Almansa, Josué C27.3 Balduini, Anna P15.4 Alonso, Ariel C5.3 Balliu, Brunilda C34.5 Alonso, R P14.1 Bamia, Christina P21.9 Altini, Mattia P8.5 Bandyopadhyay, Dipankar C23.3 Altman, Doug P20.9 Banerjee, Buddhananda C21.4 Altstein, Lily P4.15 Bare, Marisa P20.19 Ambler, Gareth C24.4, P11.5, P20.11, P20.22 Barrett, Jessica C16.4 Ambrogi, Federico P8.8 Barry, Sarah P7.3 Amieva, Helene C31.3 Bartlett, Jonathan C25.1, C25.4 Ancelet, Sophie C12.3, P20.14 Bastard, Mathieu P22.24 Andersen, Per Kragh C26.1, C6.3 Bauer, Peter P1.2 Anderson, Denise C23.5 Becher, Heiko C19.3 Anderson, John P4.20 Bekaert, Maarten C9.1 Andersson, Eva C33.3 Belgrave, Danielle C12.4 Andersson, Therese C6.2, C22.2 Belin, Lisa P4.23 Andrews, Nick P18.2 Bellocco, Rino P3.6, P12.5 Antolini, Laura I4.2 Bellomo, Rinaldo C14.3 Antweiler, Kai C33.5 Bender, Ralf C10.2, C21.3 Arden, Nigel P21.5 Benner, Axel C3.2, C28.4 Aregay, Mehreteab P18.1 Benzenine, E MS2.5 134/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info Berg, Erik P19.10 Bowman, Adrian C13.3 Bergeron, Pierre-Jérôme P22.2 Bradburn, Mike P4.30, P22.31 Bergersen, Linn Cecilie I6.2 Brayne, Carol C31.3 Bergsma, Wicher C5.1 Briones, Eduardo P20.19 Bertoni, Francesco P20.23 Brody, David P4.20 Beryl, Primrose P10.5 Broët, Philippe P4.23 Betensky, Rebecca C4.4, P22.30 Brombin, Chiara P12.2 Beyea, Jan C25.3 Brønnum, Peter P13.5 Beyersmann, Jan C32.5, P22.3, P22.13 Brouste, Véronique C32.3 Bhambra, Jasdeep K P10.1 Bruyneel, Luk P13.1 Biard, Lucie P22.25 Bryan, Susan C13.4 Billingham, Lucinda P20.17, P22.22 Buchan, Iain C12.4 Binder, Harald C3.2, C10.3, C10.4, C19.4, C32.1 Buchholz, Anika C32.1 Binder, Nadine C15.1 Buckley, Michael C29.4 Binquet, Christine P22.14 Bujkiewicz, Sylwia P15.6 Birch, Colin C12.3 Bullinger, Lars C3.2 Bishop, Christopher C12.4 Burton, Paul R C3.4, P15.7 Biswas, Atanu C14.4, C21.4 Burzykowski, Tomasz C17.1 Bjerre, Lise C7.1 Buyze, Jozefien I5.3, C9.4 Bjornstad, Jan C12.1 Cadarso-Suárez, Carmen C28.2, P16.5 Blanche, Paul C20.5 Caddick, Katharine P21.4 Blazeby, Jane P21.4 Calara, Jozer P21.4 B.N., Murthy P4.7 Calza, Stefano P2.1 Boda, Krisztina P18.3 Campbell, Michael I2.1, P8.9 Bodin, Julie P3.7 Candel, Math C14.1, P4.13 Boehringer, Stefan C34.5 Candy, Bridget P20.22 Boelaert, Marleen P15.2 Cannon, Jeff P10.3 Boers, Kim C1.4 Caria, Maria Paola P3.6 Boessen, Ruud P1.5 Carlin, John C25.5, C27.4, P11.7 Bogenrieder, Thomas P4.6 Carpenter, James C25.4 Bollerslev, Jens C13.2 Carretta, Elisa P8.5 Bongers, Mathilda P22.29 Carr, Matthew P3.8 Bonnett, Laura P20.3 Carroll, S P21.3 Boroomandnia, Nasrin P14.7 Carstensen, Bendix C4.1 Bottle, Alex I2.3 Carter, Lesley-Anne P21.10 Boucquemont, Julie P22.27 Caruana, Emmanuel P3.12 Bouillaud, Emmanuel P4.21 Casey, Neil P3.3 Boulesteix, Anne-Laure C19.4 Chadha-Boreham, Harbajan P6.3 Bouzala, GA C8.5 Chalder, Trudie P3.1 Bowden, Jack C9.2, C29.5, P1.4 Chambers, Larry C18.1 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info 135/156 Chappell, Richard C19.1 Cro, Suzie P4.12 Charlett, André P18.2 Crowther, Michael C16.2, C26.2, P12.4, P22.16 Chen, Chia-Min P1.3 Culliford, David P21.5 Chen, Ming-Hui P21.8 Custovic, Adnan C12.4 Chen, Yuh-Ing P17.1 Cyr, Diane P7.5 Chevret, Sylvie P3.12, P17.3, P20.10, P22.10 Czene, Kamila C15.3, C27.1, C27.2 Chiang, Chieh P21.4 Daikos, G C8.5 Chie, Wei-Chu P21.4 Dalveit, Anne Kjersti C15.4 Chin, Peter C4.4 Damgaard, Katrine P8.2, P8.3 Chiu, Herng-Chia P21. Danesh, John P15.9 Chi, Yunchan P1.3 Danger, Richard P20.7 Chong, Wai Fung C11.4 Darabi, Hatef P20.15 Choodari-Oskooei, Babak P1.4, P20.1 Dartigues, Jean-Francois C31.3 Christensen, Anette Luther P21.6 Davidian, Marie C5.3 Christensen, Kaare MS1.5 David, Marie-Pierre P18.1 Ciotti, Emanuele P8.5 de Blasio, Birgitte Freiesleben I1.1 Claesen, Jurgen C17.1 Debray, Thomas C24.2, P20.2, P22.22 Clark, AL P21.3 De Campos, Cassio P P20.23 Cleland, JGF P21.3 Decarli, Adriano P8.8 Clements, Mark P21.7 Declerck, Dominique C23.3 Close, Nicole C14.5 Deeks, Jon P20.17 Cnattingius, Sven MS1.6 de Hoop, Esther C30.3 Coker, Bola P11.3, P16.2 Dejardin, David C2.2, P22.11 Collignon, Olivier C15.5 de Klerk, Nicholas C23.5 Collins, Gary P4.30 de Kort, Wim P20.5 Collins, Peter P21.4 Del Greco M., Fabiola P3.11 Commenges, Daniel C8.2, C19.2 Del Rio Vilas, Victor C12.3 Congdon, Peter C12.2 de Luna, Xavier C9.3, P14.3 Cook, Andrea MS2.2 de Melker, Hester P21.13 Cooper, Ben C26.3 Denaxas, Spiros P14.6 Cooper, Matthew C23.5 den Heijer, Martin P7.2 Cooper, Nicola J P15.6 De Nisi, Martina P7.4 Copas, Andrew C25.2, C31.5, P11.6 Denison, Fiona P19.7 Cortés, Jordi P6.3 de Ruysscher, Dirk P22.29 Costantini, Anna P21.4 De Rycke, Yann P4.23, P21.12, P22.25 Cottenet, Jonathan P22.14 Descatha, Alexis P3.7 Coupe, Veerle P22.29 De Silvestri, Annalisa P15.4 Crainiceanu, Ciprian I3.2 Desjardins, Laurence P22.25 Crawford, Paul P4.20 Dethlefsen, Claus P21.6 Crispino-O'Connell, Gloria P4.20 de Uña- Álvarez, Jacobo C29.1, P22.5 136/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info Devaux, Yvan C15.5 Esnaola, Mikel C17.4, P10.4 Devesa, Susan S C32.2 Etard, Jean-François P22.24 Dewanji, Anup C4.3 Eulenburg, Christine P22.6 de Wreede, Liesbeth C C4.5 Faghihzadeh, Soghrat P14.7 Díaz Corte, Carmen P22.5 Fankhauser, Stefan P4.14 Dickman, Paul C6.2, C22.2 Didelez, Vanessa C1.1 Farrington, Paddy MS2.5, MS2.6, C8.1, C8.4, C33.4, P18.2 Diggle, Peter C16.4 Felsoci, Marian C4.2, P19.6 Dillon, Catherine P4.27 Ferenci, Tamás P21.18 DiMaio, Rebecca P4.20 Ferlini, Marco P15.4 Ding, Yew Yoong C11.4 Ferraroni, Monica P8.8 Di Serio, Clelia P12.2 Feuk, Lars C17.3 Diya, Luwis C15.3, C30.5 Field, David P20.13 Dobson, J C34.4 Fischer, Martina C28.4 Doherty, Dorota P10.3 Fleischer, Frank P4.6 Dolling, David C31.5 Forbes, Andrew C5.2, C14.3, P4.28 Dolovich, Lisa C18.1 Forbes, Catherine P4.28 Domingo-Salvany, Antònia C27.5, P21.15 Ford, Ian C5.4, P19.8 Donachie, Paul HJ P19.9 Fortiana, Josep P21.15 Doughty, RN C34.4 Foster, Jared C18.2 Douiri, Abdel P16.1, P21.1 Fotheringham, James I2.1, P8.9 Doussau, Adelaide C2.5 Foucher, Yohann C7.3, C24.1, P20.7 Draper, Elizabeth P20.13 Fraser, Christophe I1.3 Drösler, Saskia P8.7, P8.10 Friede, Tim C2.1, P1.8, P19.2 Dunn, David C31.5 Frigessi, Arnoldo I6.2, C23.4 Dunn, Graham C30.2, P3.2 Frøslie, Kathrine Frey C13.2 Durkalski, Valerie P4.27 Funatogawa, Ikuko C29.3 Dusek, Ladislav P8.4, P22.9 Funatogawa, Takashi C29.3 Eckert, Benjamin C4.4 Furstova, Jana P22.7 Egberts, Antoine CG P1.5 Galanti, Maria Rosaria P3.6 Eide, Geir Egil P19.1 Garcea, Domenico P8.5 Eijkemans, Marinus JC C1.5 Garthwaite, Paul C33.4, P18.2 Eilers, Paul C13.4, C31.1, C31.2 Gasparrini, Antonio C34.4, P21.17 Elfakir, Anissa C34.2 Gaus, Wilhelm P4.5 el Galta, Rachid P4.18 George, Julie P14.6 Elm, Jordan P4.27 Gerds, Thomas A C6.3, C20.4, P20.20 Eloranta, Sandra C22.2 Gerke, Oke C18.5 Emsley, Richard C30.2, P3.2 Gerlinger, Christoph P4.2 Enki, Doyo P18.2 Geskus, Ronald Escobar, Antonio P20.19, P20.24 C24.5, C26.5, C26.5, P12.1, P14.5, P14.5, P22.17 Giard, Caroline P21.12 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info Gilet, Hélène P6.2, P10.2 Harrison, Mark J P15.6 Giral, Magali C7.3, C24.1 Hatzakis, A C8.5 Gjerdevik, Miriam C34.1 Häusler, Gabriele P21.2 Glad, Ingrid K I6.2 Haverstock, Dan P4.26 Gladstone, Primrose Beryl P4.17 Hazelton, Martin C21.5 Gleiss, Andreas P21.2 Heaton, Nigel P21.4 Glimm, Ekkehard C33.5 Hehlmann, Ruediger P22.23 Godang, Kristin C13.2 Heinze, Georg C25.3, P22.27, P22.28 Goeman, Jelle C11.2, C28.5 Heinzl, Harald P6.3 Goetghebeur, Els I5.3, C1.3, C3.5, C9.4 Hejblum, Boris C17.2 Goldsmith, Jeff I3.2 Helgeland, Jon P8.2, P8.3 Goldsmith, Kimberley P3.1 Hellton, Kristoffer Herland C23.3 Gonzalez, Juan R C17.4, P10.4 Hemingway, Harry P14.6 Gonzalez, Nerea P20.19, P20.24 Henderson, Robin C16.4 Gorst-Rasmussen, Anders P21.11 Hendrickx, John P16.3 Götte, Heiko C20.1 Heng, Bee Hoon C11.4 Gourmelen, Julie P7.5 Henriksen, Tore C13.2 Graf, Alexandra P1.2 Herich, Lena P13.4 Gragn, Doyo C33.4 Herquelot, Eléonore P3.7 Greenop, Kathryn C23.5 Heuch, Ivar C34.1 Grobbee, Diederick E P1.5 Hicks, Andrew P3.11 Groenen, Patrick C31.1 Hieke, Stefanie C3.2 Groenwold, Rolf HH P1.5, P19.5 Hielscher, Thomas C3.2 Grönberg, Henrik P21.7 Hiemeyer, Florian P4.2 Grosch, Kai P4.21 Higgins, Julian P15.1 Grotmol, Tom C32.2 Hinchliffe, Sally R C26.2, P22.15, P22.21 G. Tahoces, Pablo P16.5 Hobkirk, J P21.3 Guedj, Jérémie C8.3 Hoes, AW P19.5 Guéguen, Alice P3.7, P7.5 Hoffmann, Verena Sophia P20.21 Gueorguieva, Ralitza C16.5 Hof, Michel P3.4, P19.4 Gulsvik, Amund P19.1 Høilund-Carlsen, Poul Flemming C18.5 Gurrin, Lyle C21.5 Holland, Fiona P21.10 Haaland, Øystein P19.10 Hopewell, Sally P4.30 Ha, Catherine P3.7 Hornbuckle, Janet P10.3 Hadjihannas, L C8.5 Hosseini, Sayed Mohsen P7.1 Häggström, Jenny C9.3 Houwing-Duistermaat, Jeanine J C34.5 Hahné, Susan I1.2, P21.13 Howe, Andrew P4.20 Haines, Linda C33.1 Hsiao, Chin-Fu P21.4 Hakulinen, Timo MS1.2 Hsu Schmitz, Shu-Fang P4.14 Hamberg, Paul C2.2 Huang, Chi-Shen P17.1 137/156 138/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info Huet, F MS2.5 Kejžar, Nataša C22.5 Humphreys, Keith P20.15 Kenward, Michael G C5.3, C29.2, C34.4, P22.26 Iacobelli, Simona C4.1 Kieltyka, Agnieszka P14.8, P21.19 Ickstadt, Katja I6.1 King, Michael P20.22 Ieva, Francesca P8.1 Klersy, Catherine P15.4 Ingel, Katharina C22.3 Klugkist, Irene P21.8 Jackson, Christohper H P10.1 Klungel, Olaf H P1.5 Jackson, Dan P11.1 Kneib, Thomas C28.2 Jackson, Lisa MS2.2 Knol, Mirjam P21.13 Jackson, Victoria P3.11 Knol, Mirjam J P1.5 Jacqmin-Gadda, Hélène C20.5, C31.3 Knorr, Silke P8.7, P8.10 Jacques, Richard I2.1, P8.9 Koenig, Franz P1.2 Jahn-Eimermacher, Antje C22.3 Koffijberg, Erik P20.2 Jaki, Thomas P4.8 Koffijberg, Hendrik C24.2 James, Ian P22.8 Kohl, Maria P22.28 Jamieson, Sarra C23.5 Koller, Michael T C20.4 Janousova, Eva P20.16, P22.9 Komarek, Arnost P20.5 Jarkovsky, Jiri C4.2, P19.6 König, Jochem C10.3, C10.4 Jelizarow, Monika C33.2 Krahn, Ulrike C10.3, C10.4 Jenkner, Carolin C19.3 Kristoffersen, Doris Tove P8.2, P8.3 Jensen, Aksel C6.3 Kropf, Siegfried C13.1, C33.5 Johansson, Anna C6.2 Kuendgen, Andrea P22.18 Johansson, Anna CV C17.3 Kuhlmann-Berenzon, Sharon P18.4 Johnston, Robert L P19.9 Kunz, Cornelia Ursula C2.1 Joly, Pierre P22.19 Kurtinecz, Milena P15.8 Jones, Elinor P3.11, P15.7 Kvaale, Gunnar C15.4 Jones, Hayley E I2.2 Lado, María J P16.5 Josefsson, Maria P14.3 Lair, Marie-Lise C15.5 Journy, Neige P20.14 Lambe, Mats C6.2 Jung, Klaus P19.2 Lambert, Jerome P20.10 Kaczorowski, Janusz C18.1 Kahan, Brennan C P4.11, P4.12 Lambert, Paul C6.2, C22.2, C26.2, P12.4, P22.15, P22.16, P22.21 Kaiser, Thomas C21.3 Landau, Sabine P3.5 Kalaycioglu, Oya C25.2 Langaas, Mette C21.1 Kalina, Jan C28.1 Lange, Theis C9.1 Kariman, Noorosadat P14.7 Langholz, Bryan I4.1 Karlsson, Robert P21.7 Lanius, Vivian P4.16 Kasparek, Tomas P20.16 Laouénan, Cédric C8.3 Kasza, Jessica C11.1 Larsen, Klaus Groes P13.2 Katina, Stanislav C13.3, P9.1 Larsen, Torben Bjerregaard P13.5 Laubender, Ruediger Paul P17.2 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info Laurent, Olivier P20.14 Looman, Caspar P20.18 Laurier, Dominique P20.14 Lorand, Daniel C14.2 Lauseker, Michael P22.18 Lorent, Marine C24.1 Lawlor, DA P19.5 Lorenz, Eva C19.3 Lawo, John-Philip P4.10 Luime, Jolanda P20.4 Lazaro, Santiago P20.19 Luna del Castillo, Juan de Dios P6.1 Leask, Kerry C33.1 Lundbye-Christensen, Søren P13.5, P21.11, P21.6 le Cessie, Saskia C1.4, P7.2 Lund, Eiliv C6.1, C31.4 Lee, Amanda J P19.7 Lydersen, Stian C21.1 Lee, Donghwan C32.4 Macgrogan, Gaëtan C32.3 Lee, J Jack C2.4 Magnusson, Patrik KE C17.3 Lee, Katherine C25.5, C27.4 Mahande, Michael Johnson C15.4 Lee, Myeongjee C27.1, C27.2 Maheswaran, Ravi I2.1 Lee, Woojoo C32.4, P2.2 Mahboubi, Amel P22.14 Lee, Youngjo C12.1, C32.4, P14.2 Majek, Ondrej P8.4 Leffondré, Karen P22.27, P22.28 Majewska, Renata P14.8, P21.19 Le Goff, Mélanie P22.19 Mandal, Saumen C14.4 Lemij, Hans C13.4 Mandel, Micha C4.4 Leon, Bobrowski C28.3 Mander, Adrian P1.6 Le Quan Sang, Kim- Hanh P6.4 Manktelow, Bradley P8.6, P20.13, P22.15 Manongi, Rachel C15.4 Lesaffre, Emmanuel C2.2, C3.3, C13.4, C16.1, C20.2, C23.3, C30.5, C31.1, C31.2, P13.1, P14.2, P15.2, P20.4, P20.5, P22.11 Mansmann, Ulrich C33.2 Maracy, Mohammad Reza P7.1 Li, Baoyue C30.5, P13.1 Mariani, Pascale P22.25 Li, Lingling MS2.3 Marioni, Riccardo C31.3 Lie, Rolv Terje C15.4, P19.10 Mariosa, Daniela P12.5 Li, Gang P4.15, P4.29, P15.8, Marque, Sebastien C34.2 Lim, T K C11.4 Marryat, Louise P7.3 Lin, Haiqun C16.5 Marson, Anthony P20.3 Lin, Lanjia C14.2 Martínez-Camblor, Pablo P22.5 Lin, Yunzhi C19.1 Martinez, Carlos P22.1 Li, Qiyu P4.14 Masca, Nicholas C3.4 Liquet, Benoit C19.2 Masenga, Gileard C15.4 Littnerova, Simona C4.2, P19.6 Maskell, Joe P21.5 Liu, Aihua P21.14 Matawie, Kenan P13.1 Liu, Hanhua C30.2 Mathoulin-Pelissier, Simone C20.3, C32.3 Liu, Jen-pei P4.1 Matthews, Fiona E C31.3, P10.1 Liu, Tianqing C21.1 Maucort-Boulch, Delphine C22.5 Li, Yuanzhang C21.1 Mauguen, Audrey C20.3, C32.3 Lloyd, Suzanne C5.4 Mayer, Jiri P22.9 139/156 140/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info Mazroui, Yassin C32.3 Murawska, Magdalena C16.1 McCallum, Emma P1.6 Murray, Heather P19.8 McCanny, Paul P4.20 Musoro, ZJ C24.5, P12.1 McEneaney, David P4.20 Mutsvari, Timothy C23.3 McKinnon, Elizabeth P19.3, P22.8 Nanni, Oriana P8.5 McNamee, Roseanne P3.8, P21.10 Nardi, Alessandra C27.5 Meijer, Rosa C28.5 Nasiri, Malihe P14.7 Menten, Joris P15.2 Nasserinejad, Kazem P20.5 Mentré, France C8.3, P22.12 Nelson, Jennifer C MS2.2 Mercier, Francois C4.4 Neuenschwander, Beat C14.2 Metcalfe, Chris C5.5 Ng, Danice P11.1 Metzger, Marie P22.27 Nguyen, Cattram C25.5 Meyer, Haakon E C6.4 Nguyen, Michael MS2.1 Miklik, Roman C4.2 Nicholl, Jon I2.1, P8.9 Mikoshiba, Naoko P21.4 Nieboer, Daan P20.2, P20.25 Milanzi, Elasma C5.3 Niebuhr, David C21.1 Milne, Elizabeth C23.5 Nijman, Ruud G P20.25 Minelli, Cosetta P3.11 Njagi, Edmund Njeru C29.2 Mitrani-Gold, Fanny P15.8 Noh, Maengseok P14.2 Mmbaga, Blandina Theophil C15.4 Nonogi, Hiroshi P4.19 Mockenhaupt, Maja C7.4 Noordzij, JP P20.17 Modi, Neena C11.2 Noufaily, Angela C33.4, P18.2 Mogensen, Ulla B P20.20 Novianti, Putri P15.5 Mohd Din, Siti Haslinda P20.4 Nuel, Gregory C31.4, P6.4 Molas, Marek P14.2, P20.4 Nyári, Tibor P18.3 Mol, Ben Willem C1.4, P22.17 Nyberg, Lars P14.3 Molenberghs, Geert C29.2, C5.3, P18.1 Oakes, David C18.3 Mollema, Liesbeth P21.13 Oberije, Cary P22.29 Moons, Karel C24.2, P19.5, P20.2, P22.22 Obure, Joseph C15.4 Morabito, Alberto P4.22 Ofuya, Mercy P15.3 Moradpour, Farhad P7.1 Ohneberg, Kristin P22.13 Moran, John L C11.1 Ohuma, Eric P20.9 Moreira, Carla C29.1 Omar, Omar P4.30 Morgagni, Paolo P8.5 Omar, Rumana Z C24.4, C25.2, P20.11, P20.22 Morgan, Ann C34.5 Oostenbrink, Rianne P20.25 Morris, Tim P4.11, P11.8 O'Neill, Phil I1.2 Mrozek-Budzyn, Dorota P14.8, P21.19 Orellana, Liliana I5.2 Muche, Rainer P4.5 Ou, Phalla P6.4 Müller, Tina P4.10 Overvad, Kim P21.6, P21.11, Mundy, Linda P4.29, P15.8 Ozol-Godfrey, Ayca P11.2 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info Paganoni, Anna P8.1 Prague, Mélanie C8.2 Palesch, Yuko P1.1, P4.4, P4.27 Pramana, Setia C17.3 Palomar, Mercedes C26.3 Pramstaller, Peter P P3.11 Pal, Rupam Ranjan C14.2 Prevost, Toby C29.5, P3.3 Pantazis, Nikos P21.9, P21.16, P22.26 Pripp, Are Hugo C30.4 Paoletti, Xavier C2.5 Proust-Lima, Cécile C16.3, C19.2, C31.3 Papachristofi, Olympia C18.4 Psichogiou, M C8.5 Pap, Ákos Ferenc P11.4 Putter, Hein C4.5 Pardo, MC P14.1 Pye, Karen C2.3 Parenica, Jiri C4.2, P19.6 Quantin, Catherine MS2.5, P22.14 Parker, Robert P4.24 Quintana, Jose Maria P20.19, P20.24 Park, Eunsik P1.9 Qvigstad, Elisabeth C13.2 Parmar, Mahesh KB P1.4, P20.1 Rahman, Shafiqur P20.11 Parsons, Nicholas C2.1 Raisaro, Arturo P15.4 Pavlik, Tomas P22.9 Raja, Edwin Amalraj P19.7 Pavlou, Menelaos P11.6 Ramirez, Guillermo P22.1 Pawitan, Yudi C3.1, C17.3, C32.4, P2.1, P2.2 Ramsay, Craig P4.28 Peacock, Janet P15.3 Rancoita, Paola M V P12.2, P20.23 Pebody, Richard C8.1 Rapsomaniki, Eleni P14.6, P15.9 Pedersen, Nancy L MS1.4 Rauch, Geraldine P22.3 Pellicori, P P21.3 Ravelli, Anita P19.4 Pertile, Riccardo P7.4 Rebora, Paola I4.2, C27.1, C27.2 Pfirrmann, Markus P22.23 Regnault, Antoine P6.2, P10.2 Phillips, Alan C30.1 Rehal, Sunita P4.12 Pibouleau, Leslie P17.3 Reiczigel, Jenö P21.18 Pichler, Irene P3.11 Reilly, Marie C15.3, C27.1, C27.2 Pickles, Andrew P3.1 Reimnitz, Peter P4.16 Piffer, Silvano P7.4 Remontet, Laurent P22.14 Piperno-Neumann, Sophie P22.25 Resche-Rigon, Matthieu P3.12, P22.10 Pirracchio, Romain P3.12 Richardson, Sylvia I6.2, C12.3 Plancade, Sandra C31.4 Riecansky, Igor P9.1 Plancher, Corine P22.25 Rietbergen, Charlotte P21.8 Ploner, Meinhard C25.3 Riley, Richard C24.2, P20.17, P22.22 Pocock, Stuart C7.2, C34.4 Ring, Arne P14.4 Pohar Perme, Maja C22.1 Rippe, Ralph P7.2 Pokhrel, Arun P22.20 Rivadeneira, Fernando C31.1 Poon, Ronnie P21.4 Rizopoulos, Dimitris C16.1, C29.2 Poppe, K C34.4 Robins, James I5.2 Portengen, Lützen C27.3 Roca-Pardiñas, Javier P16.5 Pradhan, Biswabrata C4.3 Rockova, Veronika C3.3 141/156 142/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info Rodriguez-Girondo, Mar C28.2 Schwarz, Daniel P20.16 Roes, Kit CB C1.5, P1.5, P15.5 Scotti, Valeria P15.4 Rogers, Jennifer C7.2 Scudelle, Luigia P15.4 Røislien, Jo C13.2 Seaman, Shaun C9.2, C25.1, C25.4, P11.6 Roldán Nofuentes, Jose Antonio P6.1 Seaton, Sarah P8.6, P20.13, P22.15 Romaniuk, Helena P11.7 Sekula, Peggy C7.4 Romio, Silvana P3.6 Séne, Mbéry C16.3 Rondeau, Virginie C20.3, C32.3 Senn, Stephen IP1, C15.5, C30.1 Roquelaure, Yves P3.7 Sermeus, Walter C30.5, P13.1 Rosendaal, Frits P7.2 Shah, Anoop P14.6 Rosenheck, Robert C16.5 Shanyinde, Milensu P4.30 Roth, Katrin P4.16 Sharpe, Michael P3.1 Rotnitzky, Andrea I5.2 Sharples, Linda C18.4 Røysland, Kjetil C1.2 Sheehan, Nuala A C3.4, P3.11, P15.7 Royston, Patrick P1.4, P4.9, P11.8, P20.1 Shkedy, Ziv P18.1 Ruberg, Stephen C18.2 Siannis, Fotios P21.9 Rücker, Gerta C10.1 Siemiatycki, Jack P21.14 Ruijs, Helma I1.2 Sikorska, Karolina C31.1 Rutherford, Mark P22.16 Simon, David P22.30 Safavi, Nastaran P14.7 Simpson, Angela C12.4 Salim, Agus I4.3, C3.1, P2.1 Singh, Krishan P4.29, P15.8 Samoli, Evi P21.16 Singh, Rajvir P22.4 Samuelsen, Sven Ove C6.4, C6.5 Sitta, Rémi P3.7, P7.5 Sanchez-Niubo, Albert C27.5, P21.15 Skaug, Knut P19.1 Sangalli, Laura I3.3 Skinner, Jason C17.2 Santhakumaran, Shalini C11.2 Skjærven, Rolf MS1.1, MS1.7 Sarasqueta, Cristina P20.19, P20.24 Small, Robert D P11.2 Sauerbrei, Willi C19.3, C19.4, C32.1, P4.9 Smeeth, Liam P14.6 Sauzet, Odile P15.3 Smith, Karen P4.25 Savignoni, Alexia P21.12 Smits, Gaby P21.13 Scalia-Tomba, Gianpaolo I1.1, C27.5 Snell, Kym P22.22 Schemper, Michael C15.2, P21.2 Solari, Aldo C11.2 Scherjon, Sicco C1.4 Solomon, Patricia J C11.1 Schetelig, Johannes C4.5 Sørensen, Helle I3.1 Schlenk, Richard F C3.2 Sørensen, Øystein C23.4 Schmelter, Thomas P4.2 Sparrow, John M P19.9 Schmidli, Heinz P1.8 Speed, Doug I6.3 Schneider, Simon P1.8 Speed, Terry IP2 Schumacher, Martin C3.2, C7.4, C15.1, C26.3, C32.5, P22.13 Spinar, Jindrich C4.2, P19.6 Spiegelhalter. David J I2.2 Schürmann, Christoph C21.3 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info 143/156 Stahl, Daniel C24.3 Timmis, Adam P14.6 Stallard, Nigel C2.1 Tinelli, Carmine P15.4 Stare, Janez C22.5 Titterington, Mike C19.5 Steegers-Theunissen, Regine C31.2 Tjomsland, Ole P8.2 Stefanic, Martin P4.6 Tjønneland, Anne P21.11 Sterne, JAC P19.5 Todd, Susan C2.1 Steyerberg, Ewout W P20.2, P20.8, P20.12, P20.18, P20.25 Torres-Martin, Juan V P6.3 Touloumi, Giota C26.4, P3.9, P21.16, P22.26 Stirnemann, Jérôme P22.12 Touraine, Célia P22.19 Stocken, Deborah P22.22 Trandafir, Camelia C14.4 Støer, Nathalie C C6.4, C6.5 Trébern-Launay, Katy C7.3 Stott, David C5.4 Tretli, Steinar C32.2 Stratton, Irene M P19.9 Trinquart, Ludovic P21.4 Strelkowa, Natalja P4.6 Trneny, Marek P22.9 Sturtz, Sibylle C10.2 Tsiatis, Anastasios A C5.3 Suchanek, Stepan P8.4 Tsonaka, Roula C34.5 Sun, Hong P4.14 Tsoumanis, Achilleas P18.4 Suo, Chen P2.1 Tubert-Bitter, Pascale MS2.5, P21.12 Sutton, Alex J P15.6 Tudur Smith, Catrin P20.3 Swihart, Bruce I3.2 Turner, Robin M P4.3 Swinkels, Sophie P16.3 Unkel, Steffen C8.1, C8.4 Symmons, Deborah P M P15.6 Ursin, Giske MS1.3 Sypsa, Vana C8.5 Uyl-de Groot, Carin P22.29 Tabor, Bruce C29.4 Vach, Werner C18.5, P4.17, P10.5 Taiyari, Khadijeh C24.4 Vaillant, Michel C15.5 Taubel, Jorg P14.4 Valberg, Morten C32.2 Taylor, Jeremy C18.2 Valenta, Zdenek C28.1, P22.7 Taylor-Robinson, David C16.4 Valsecchi, Maria Grazia C27.2 Teerenstra, Steven C30.3 Van Belle, Vanya P20.18 Teo, Shu Mei C3.1 van Boven, Michiel I1.2 Thabane, Lehana C18.1, P5.1 van Bockxmeer, Frank C23.5 Thalabard, Jean- Christophe P6.4 van Breukelen, Gerard C14.1, P4.13 Therneau, Terry C22.4 Van Calster, Ben P16.4, P20.12, P20.18 Thiebaut, Rodolphe C2.5, C8.2, C17.2 van den Bor, Rutger M C1.5 Thompson, John R P3.11, P15.6 Van den Heede, Koen C30.5, P13.1 Thompson, Lucy P7.3 van der Baan, Frederieke H P1.5 Thompson, Simon P3.3, P15.9 van der Klis, Fiona P21.13 Thoresen, Magne C23.3, C23.4 van der Tweel, Ingeborg P15.5 Tibaldi, Fabian C5.1, P18.1 van der Wal, Willem M C1.5 Tilling, K P19.5 VanderWeele, Tyler I5.1 Timmerman, Dirk P16.4, P20.12 144/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info van der Woude, Diane C34.5 Wei, Yinghui P15.1 van Geloven, Nan P22.17 Weldeselassie, Yonas G MS2.6 Van Hoorde, Kirsten P20.12 Weng, Yanqiu P4.4 Van Huffel, Sabine P16.4, P20.12 Werft, Wiebke C28.4 Van Keilegom, Ingrid C29.1 Weschenfelder, Ann-Kathrin P8.7 van Klaveren, David P20.8 Wetherington, Jeffrey P4.29, P15.8 Van Oirbeek, Robin C20.2 Weyermann, Maria P8.7, P8.10 Van Rompaye, Bart C3.5 Wharton, Rose P4.30 Vansteelandt, Stijn I5.3, C1.3, C9.1 Wheeler, Graham P1.7 van Zwet, Erik C11.2 Whitaker, Heather J MS2.6, C8.1, C8.4 Varewyck, Machteld I5.3, C1.3 Whitehead, Anne C2.3 Vargha, Péter C23.1 Veierød, Marit Bragelien C13.2, C32.2 White, Ian C5.2, C9.2, C25.1, C25.4, P11.1, P11.8, P15.9 Velicko, Inga P18.4 White, Jane P7.3 Velten, M MS2.5 White, Peter P3.1 Verbeke, Geert C5.3, C29.2 Wieseler, Beate C21.3 Verde, Pablo P15.10 Wiklund, Fredrik P21.7 Vergouwe, Yvonne P20.2, P20.8, P20.12, P20.18, P20.25 Willemsen, Sten C31.2 Williamson, Elizabeth C5.2, C21.5 Vermeer, Koen C13.4 Williamson, Paula P20.3 Vermeulen, Roel C27.3 Wilson, Philip P7.3 Vervölgyi, Elke C21.3 Wilcox, Allen J MS1.7 Vervölgyi, Volker C21.3 Wisniewska, Dominika P11.2 Verweij, Jaap C2.2 Witteman, Jacqueline C M C20.4 Vigan, Marie P22.12 Wittkowski, Knut C17.5 Vilar, Jose A P20.6 Woelber, Linn P22.6 Vilar, Juan M P20.6 Woertman, Willem C30.3 Vilgrain, Valerie P21.4 lbers, Marcel C20.4 Vourli, Georgia P3.9 Wolfe, Rory P11.5 Voysey, Merryn P4.30 Wolfsegger, Martin P4.8 Waaijenborg, Sandra P21.13 Wolkewitz, Martin C26.3 Wagner, Daniel C15.5 Wood, Angela P15.9 Wallinga, Jacco i1.2, P21.13 Worthington, Jane C34.5 Walter, Stephen D P4.3 Wynants, Laure P16.4 Wang, Duolao P14.4 Xiao, Yongling C7.1 Wang, Jixian P4.21 Xu, Stanley MS2.4 Wang, Sijian C19.1 Yauy, Kevin P6.4 Wang, Weiwu P12.5 Ye, Weimin P12.5 Wang, Yanzhong C19.5 Yokoyama, Hiroyuki P4.19 Wason, James P1.6 Yonemoto, Naohiro P4.19 Wegscheider, Karl P13.4, P22.6 Younger, Jaime P22.2 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info 145/156 Yuasa, Haruyuki P4.19 Zhang, J P21.3 Yu, Ly-Mee P4.30, P22.31 Zhao, Wenle P1.1, P4.4, P4.27 Yu, Menggang C19.1 Zins, Marie P7.5 Yu, Onchee MS2.2 zu Eulenburg, Christine P13.4 Zangogianni, Marina P21.9 Zuma, Khangelani C7.5 Zavoral, Miroslav P8.4 Zwiener, Isabella C20.1 Zayeri, Farid P14.7 Zwinderman, Aeilko C24.5, P3.4, P12.1, P19.4, P22.17 146/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info Information for Presenters Instruction for oral presentations You can find the session in which your presentation is scheduled on the website of the conference and in the conference program. Please make your presence known to the session chair at least 10 minutes before your session starts and be present during the entire session in which your presentation is scheduled. Length of presentation – – – Invited presentations have 30 minutes available, including time for questions - i.e. the talk should be around 25 minutes. Contributed presentations have 18 minutes available, including time for questions – i.e. the talk should be around 15 minutes. Plenary speakers have specific time limits. Preparation of presentation The PCs used for the presentations are running Windows Vista or Windows 7. To ensure optimal software compatibility and support, presentations must be in one of the following formats: – Microsoft Office PowerPoint (version 97-2003, 2007 or 2010) for PC. – Any Adobe Acrobat PDF documents (newer versions preferred) We strongly recommend that you use standard fonts provided by Microsoft Office as this will guarantee full support on our computer systems. For PowerPoint presentations we recommend that you also bring a pdf-version as backup if possible. If you have video files in your PowerPoint presentation it must be in a format supported native under Windows Vista/Windows 7. Preferred format is Windows Media Video (.WMV) format. QuickTime (.MOV) is not supported! Transfer of presentation All invited and contributed oral presentations will be using computers provided by the Conference organization. No personal laptop or notebook computers will be allowed for invited and contributed oral presentations (exception may be done in “emergency cases”). Course holders and plenary session speakers may use their own computers. Speakers should visit the Speaker Ready Room at least two hours (preferably one day) before their scheduled presentation time. Look for direction signs to the Speaker Ready Room in the Grieg Hall. Hours of operation are as follows: Sunday, 19 August 16:00 – 20:00 Monday, 20 August 08:00 – 17:00 Tuesday, 21 August 08:00 – 13:00 Wednesday, 22 August 08:00 – 17:00 Thursday, 23 August 08:00 – 12:00 Authors should clearly identify themselves and specify the room, date and time of presentation. We suggest that you use the following name convention for your presentation: Day_Session_Number_Family name. For instance Mon_C06_3_Smith (Monday, contributed session 6, talk number 3, by Smith). If your presentation has supporting video and audio files, please remember to include these files along with your presentation. Gather all your files in one folder and make a test run of your presentation from this folder before you submit it. We recommend that you bring a backup copy of your presentation on a memory stick at your presentation. If you like to submit your presentation before the conference starts you can send your presentation by e-mail to [email protected] no later than Friday 17 August 2012. Use as subject of the e-mail: "ISCB33 Day_Session_Number_Family name" (e.g. "ISCB33 Mon_C06_3_Smith"). Equipment in the presentation room All conference rooms are equipped with the following: – One computer fed from the central server – One large screen – One LCD projector – One podium microphone – One wireless lavalier microphone – One laser pointer ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info 147/156 Instructions for poster presentations Posters will be on display in the poster area on level 2 in the Grieg Hall during the entire conference. You should install your poster as soon as possible upon arrival - the poster boards will be up from Sunday at 13.00. You should install your poster on the poster board marked with your poster number. You find your poster number in the conference program. A map of the poster placement is found on page 155. During the breaks refreshments are served in the poster area and we encourage all poster presenters to be near their posters during these breaks. In particular all poster presenters have to be at their poster during the poster session Tuesday, August 21, 10.00-11.00. Deadline for removal of posters is Thursday 23 August at 13.00. The conference organizer is not responsible for posters not collected at the end of the conference. Poster board dimensions (including frames) will be 100 cm in width and 250 cm in height. To keep good legibility of the poster, we recommend a maximum size of 95 cm (37.4 inches) in width and 100 cm (39.4 inches) in length. Tape for fastening the posters will be available in the poster area. Statistics in Medicine Special Issue People who give invited or contributed presentations (oral as well as poster) are invited to submit a paper for a special issue of Statistics in Medicine. Deadline for the submission is October 31st 2012. The standard rules and procedures for submission will hold with clear emphasis on the quality of the paper. See the Author’s guide for further information. ISCB Awards Student Conference awards (SCA) Name Country Title Session Magdalena Murawska The Netherlands Dynamic Prediction Based on Joint Model for Categorical Response and Time-to-Event C16.1 Michael Crowther UK Adjusting for measurement error in baseline prognostic biomarkers: A joint modelling approach C16.2 Yunzhi Lin USA Advanced Colorectal Neoplasia Risk Stratification by Penalized Logistic Regression C19.1 Conference Awards for Scientists (CAS) Name Country Title Session 1. Jan Kalina Czech Republic Robust Gene Selection Based on Minimal Shrinkage Redundancy C28.1 2. Kerry Leask South Africa Modelling Overdispersion in Wadley's Problem with a BetaPoisson Distribution C33.1 3. Péter Vargha Hungary Regression toward the mean and ANCOVA in observational studies C23.1 148/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info Acknowledgements Bergen Tourist Board Kongress & Kultur ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info 149/156 Conference Venue The conference will be held in Bergen, Norway in the well-known conference and concert hall Grieghallen, Edvard Griegs Plass 1, 5015 Bergen, Norway. The Grieghallen is located easily accessable in the city centre within 10 minutes walking distance from both the railway and the central bus station. From most of the recommended hotels you will not have to walk more than 15-20 minutes. How to get there from the airport By Bus. There are airport coaches departing every 15 minutes to the city centre during most of the day, and corresponding to flight arrivals in the evening. The closest airport bus stop is the central bus station. Additionally, there are less expensive local buses, which do not go directly to the city centre, i.e. require bus changes. Those timetables are found at skyss.no. By Taxi. The price for taxi from the Flesland airport to the conference venue should be about 350 NOK (~45€). How to get there from the train or bus. From both the train and the central bus station it is not more than a 10 minutes walk 150/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info General Information Getting there By Air Bergen Airport Flesland (BGO) handles the flights of SAS, KLM, Norwegian Air Shuttle, Widerøe as well as a number of other airlines (list). It is located 20 km south of the city centre. There is a good connection between the airport and the city centre by buses and taxis. The airport shuttle bus departs every 15 minutes, and the ride is 30 minutes. A one-way ticket is NOK 95 (~12€) and a return-ticket NOK 150,- (~20€). The return-ticket is valid for a month. The bus driver will announce the most important destinations in the city center. If you ask, your stop will be announced as well. Taxi fare from the airport to the city centre is approximately NOK 350,(45€). Airport bus and taxis can be paid in the vehicle with a VISAcard. By Land Train. There is one train line connecting Bergen with Eastern Norway and Oslo. All trains are operated by the Norwegian State Rail NSB. At nsb.no you find time tables and can book domestic train tickets. Bus. There are various express bus connections to the most places in southern Norway. Operators are Nor-way bussekspress, Skyss and Fjord1. By Sea After years with a vital ferry traffic till and from Bergen, unfortunately, these services are reduced to one international connection between Bergen and Hirtshals (Denmark) operated by Fjordline. Getting around in Bergen In the city centre of Bergen most distances do not require more than a 20 minutes walk. Otherwise there are good local bus connection from the city centre to most areas in the greater city area as well as a new light rail, where the first line was opened in june 2010 and which will expand during the following years. Maps and time tables for all connections are found at skyss.no. A 90-min-ticket in Bergen costs 27 NOK (~3€). The bus station may be somewhat crowded due to modification work. Practical Information Accomodation Please use the accomodation information on our webseite (http:www.iscb201.info) or contact Gabriele Zenisek: [email protected] at the conference secretariat. Certificate of Attendance Certificates of attendance will be available at the registration desk for all participants. Climate Due to its coastal location, Bergen has a maritime climate, i.e. mild winters and rather cool summers as well as a quite high precipitation, mostly in the autumn and winter months (more details). Usually July and August are warmest months in Bergen and not among the most humid. Nevertheless, in Bergen you always have to be alert to meet some rain, even in summer. Currency and Banking The official currency in Norway is the Norwegian krone (norske kroner, 8 NOK ~ 1€). International credit cards are accepted in the most hotels and restaurants, not necessarily in all shops, but you can take out cash at all bank offices and the ATMs almost everywhere. Electricity Norway uses a 230 volt 50 Hz system. Sockets are the standard European type and two-prong round pin plugs, with a hole for a male grounding pin, are standard. To use electric appliances from your country you may need a special voltage converter with an adapter plug. (wikipedia) ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info 151/156 Health Services Norway has a well-organized health care system. More detailed information about access and costs are found at the website of the Agency for Public Management and eGovernment. Language The official language of the Congress is English without translations. In the hotels, shops, busses or taxis most people are able to speak English. Lunches and Refreshments Lunches and refreshments (Monday-Wednesday) are included in the registration fee. Participants requiring a special diet are requested to mention this on their registration form. Passport and Visa All information about visa rules in Norway are found at the pages of the Norwegian immigration authorities including a list of the countries without a visa requirements. Time Zone Norway is in Central European time zone one hour ahead of GMT. At the time of the congress this will be GMT +2 due to Summer Daylight Saving Time. Vocabulary - Ordbok Norsk English Francais Deutsch Hei Hello Bonjour Hallo Ha det (bra) Goodbye Au revoir Auf Wiedersehen Hvordan går det? How are you? Comment allez-vous? Wie geht es? Takk Thank you Merci Danke Vær så god Please S'il vous plait Bitte Ja Yes Oui Ja Nei No Non Nein Hvor er …? Where is …? Où est …? Wo ist …? Snakker du …? Do you speak …? Parlez-vous …? Sprechen Sie …? Engelsk, Fransk, Tysk English, French, German Anglais, Francais, Allemand Englisch, Französisch, Deutsch Jeg forstår ikke I don't understand Je ne comprends pas Ich verstehe nicht Jeg trenger en lege I need a doctor J'ai besoin d'un docteur Ich brauche einen Arzt E, to, tre, fire, fem One, two three, four, five Un, deux, trois, quatre, cinq Eins, zwei, drei, vier, fünf Det regner. It rains. Il pleut. Es regnet. Har du en paraply? Do you have an umbrella? Avez-vous un parapluie? Haben Sie einen Regenschirm? 152/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info Social Events Reception Monday 20 August 2012 19:00 - 20:30 Håkonshallen, Bergen Conference dinner Wednesday 22 August 2012 19:00 – 23:00 Grieghallen Conference Trips, Tuesday, 21 August 2012 Short trip 1: City walk in Bergen We invite you for a classical round trip, first by bus then on foot, through picturesque streets and pass well-known and famous sites including a walk through the famous Bryggen quarter. Short trip 2: Troldhaugen – the home of Edvard Grieg The most famous Norwegian composer Edvard Grieg was born and has been living in Bergen. His home is comprising an exhibition centre with shop and café, concert hall, composers’ cabin and Grieg’s villa in an idyllic surrounding. Unfortunately, we had to cancel this trip. Sorry. ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info 153/156 Short trip 3: Bergen – City of Arts Bergen is rich in culture and the fine arts, ranging from giants such as Picasso and Munch to exciting local artists with their own ateliers. Short trip 4: Bergen – Gateway to the Fjords The Norwegian fjords are a spectacular, breathtaking experience, and Bergen is indeed the gateway to them! This trip will allow you to discover at least little part of these world famous attractions. Short trip 5: On the roof of Bergen Join our walk over the roof of Bergen, enjoying spectacular views over the the fjords as well as the mountains around Bergen. Pre and Post Conference Trips Norway in a nutshell Rosendal Hardanger Hurtigruten These trips are not included in the conference trip programme but we can recommend these attractions since you are once in Norway. 154/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info Map of Bergen ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info 155/156 Posters and Exhibitors Placement All Exhibitions are placed at level 2 156/156 ISCB 33 – 19-23 August 2012, Bergen, Norway – www.iscb2012.info Natural effect propagation Plan Grieghallen Foyer Session rooms Meeting rooms FPG Foyer Peer Gynt (Level 2) STT Trolltog (Level 3) SN Salon Nina (Level 3) F1PG Foyer 1 Peer Gynt (Level 1) SPG Peer Gynt (Level 2) SE Salon Edvard (Level 3) FUPG Foyer U Peer Gynt (Level U) SKK Klokkeklang (Level U) SBK Bukken (Level 3) FS Foyer Spissen SG Gjendine (Level U) SH Halling (Level 3) SST Småtroll (Level U) SS Svane (Level 3) Preview room SBG Bøygen (level 2) ISBN: 978-82-8045-026-5