٧ - AIFA Agenzia Italiana del Farmaco

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Ministero della Salute
DIREZIONE GENERALE DELLA VALUTAZIONE DEI MEDICINALI E DELLA FARMACOVIGILANZA
Sperimentazione Clinica
Programma del Workshop rivolto ai biostatistici operanti presso i Comitati etici
“Le competenze biostatistiche nell’ambito dei Comitati etici locali”
Roma, 18 marzo 2003
Hotel Universo, Via Principe Amedeo 5/b
9.30 Registrazione dei partecipanti
10.00 Introduzione al corso
dr. Tomino
10.10 Dalla superiorità alla non-inferiorità: problemi aperti
prof. Marubini
10.40 Disegni sequenziali per gruppi
prof. Grigoletto
11.10 Aggiustamenti dei livelli di significatività e di
confidenza: necessità e opportunità
dr. Cesana
11.40 Ruolo di differenti insiemi di pazienti nell’analisi delle
sperimentazioni cliniche
dr.ssa Bacchieri
12.10 Dibattito generale
13.00 Pausa pranzo
14.30 Costituzione dei gruppi di lavoro su:
Organizzazione delle attività formative
prof.ssa Marinoni - dr. Raschetti
Revisione delle linee guida
dr.ssa Menniti Ippolito - dr.ssa Bacchieri
Armonizzazione del comportamento dei biostatistici
nel contesto decisionale dei Comitati etici
prof. Marubini - dr.ssa Patarnello
Organizzazione dello scambio informatizzato di
commenti e pareri
prof. Grigoletto - dr.ssa Rinieri
16.30 Dibattito generale
17.30 Conclusione dei lavori
in collaborazione con
ISS (Istituto Superiore di Sanità)
SISMEC (Società Italiana di Statistica Medica ed Epidemiologia Clinica)
http://oss-sper-clin.sanita.it/
Sommario
Sperimentazioni Cliniche dei Medicinali:
Attualità e prospettive
dr. Tomino
Dalla superiorità alla non-inferiorità: problemi aperti
prof. Marubini
Disegni sequenziali per gruppi
prof. Grigoletto
Aggiustamenti dei livelli di significatività e di confidenza:
necessità e opportunità
dr. Cesana
Riferimenti bibliografici
CPMP – Points to consider on multiplicity issues
in clinical trials
ICH Harmonised Tripartite Guideline – Statistical
principles for clinical trials
Ruolo di differenti insiemi di pazienti nell’analisi delle
dr.ssa Bacchieri
Gruppi di lavoro
Organizzazione delle attività formative
prof.ssa Marinoni – dr. Raschetti
dr.ssa Menniti-Ippolito – dr.ssa Bacchieri
Armonizzazione del comportamento dei biostatistici nel
contesto decisionale dei Comitati etici
prof. Marubini – dr.ssa Patarnello
Organizzazione dello scambio informatizzato di commenti
e pareri
prof. Grigoletto – dr.ssa Rinieri
a cura di Simona de Gregori e Roberta Coppari
Sperimentazioni Cliniche
dei Medicinali:
dr. Tomino
Roma, 18 Marzo 2003
“Le competenze biostatistiche nell’ambito dei CE locali”
Carlo Tomino
Sperimentazioni Cliniche dei Medicinali:
Direzione
Direzione Generale
Generale Valutazione
Valutazione Medicinali
Medicinali ee Farmacovigilanza
Farmacovigilanza
Ministero
Ministero della
della Salute
Salute
Direzione Generale Valutazione Medicinali e Farmacovigilanza
Roma 2003
Osservatorio Nazionale sulla Sperimentazione Clinica
Informazione
Rapporto Annuale delle
Sperimentazioni Cliniche dei Medicinali
(dicembre)
■
Bollettino Sperimentazione clinica dei
medicinali (aggiornamenti semestrali)
disponibili (italiano / inglese) sul sito:
http://oss-sper-clin.sanita.it
Roma 2003
■
Roma 2003
SC presentate nel 2° Rapporto Annuale
N. SC
2000
544
2001
571
I sem 2002
305
Totale
1420
Anno
Roma 2003
Tipologia delle SC Multicentriche
in Italia
42.1%
Studi Multicentrici Internazionali
57.9%
Studi Multicentrici Nazionali
Roma 2003
Distribuzione SC in Italia per fase
Fase III
Fase II
Fase IV
Bioeq/Biod
Fase I
55.8%
31.7%
8.2%
3.0%
1.3%
Roma 2003
Distribuzione SC in Italia
per Fase e per Anno
2001
I Sem 2002
Fase III
61.6%
52.2%
52.5%
Fase II
27.8%
33.1%
36.1%
Fase IV
7.9%
10.5%
4.6%
Bioeq/Biod
1.8%
3.3%
4.3%
Fase I
0.9%
0.9%
2.6%
2000
Roma 2003
SC per tipo di sponsor
76.7%
7.8%
7.0%
4.1%
2.5%
2.0%
Azienda farmaceutica
IRCCS (pubblico-privato)
ASL
Associazione scientifica
Università
Altro
Roma 2003
SC in Italia per Gruppo Anatomico Principale
Azienda farmaceutica / Sponsor no profit
%
No profit
63.1
5.4
9.1
3.2
5.0
3.2
3.2
0.9
3.2
0.6
2.8
0.3
Roma 2003
Comitati Etici
Antineoplastici ed immunomodulatori
Sistema Nervoso
Antimicrobici per uso sistemico
App. gastroint. e metabolismo
Sistema cardiovascolare
Sangue ed organi ematopoietici
Sist. muscolo-scheletrico
Sist. genito-urinario ed ormoni sessuali
Sistema respiratorio
Vari
Organi di senso
Prep. ormonali sistemici (escl. orm. sessuali)
Dermatologici
Farmaci antiparassitari, insetticidi, repellenti
%
Az. farm.
21.4
13.0
12.4
10.3
8.5
6.7
6.4
6.3
4.6
3.7
2.4
2.4
1.5
0.3
Roma 2003
Comitati Etici presenti accreditati = 289
n. CE
% sul totale
Lombardia
Lazio
Sicilia
Veneto
Campania
Puglia
Emilia Romagna
55
32
27
24
23
19
18
19.0%
11.1%
9.3%
8.3%
8.0%
6.6%
6.2%
Altre regioni
91
31.5%
Regione
Roma 2003
Decentramento autorizzativo
sui medicinali vengono approvate direttamente
dai Comitati Etici Locali”
Roma 2003
“Oggi oltre il 95% delle sperimentazioni cliniche
Analisi della situazione
74.8% del totale delle sperimentazioni
I primi 30 Comitati Etici sono coordinatori del
Roma 2003
Team Work
Sponsor
Sperimentatori
Regioni
Ministero
della Salute
Roma 2003
CE
Direttiva 2001/20/CE (4/4/2001)
Art. 11 - Banca Dati Centrale Europea
tutti i paesi membri inviano i dati delle SC svolte nel
proprio paese alla BDCE (l’Italia tramite l’OsSC)
Roma 2003
Roma 2003
Nuova versione dell’OsSC
Banca Dati Centrale Europea
Roma 2003
Nuovo OsSC
Principali novità
Profilo ISS (per SC di fase I)
Profilo ASL (per SC in MMG e PLS)
Profilo CRO (supporto alle aziende)
Utilities per i CE
Registrazione reazioni avverse
Reportistica on-line (per Regione, singola
struttura, azienda, CE, etc.)
Roma 2003
Dalla superiorità alla non-inferiorità:
problemi aperti
prof. Marubini
Dalla superiorità alla non inferiorità: problemi
aperti
Prof. Ettore Marubini
Istituto di Statistica Medica e Biometria
Università degli Studi di Milano
Min Sal 18/03/03 (1)
NEW ENGLAND JOURNAL of MEDICINE
16th
october 1997
vol. 337
•A comparison of continuous infusion of alteplase with
double-bolus administration for acute myocardial infarction
(COBALT investigators) pag. 1124-1131.
•A comparison of reteplase with alteplase for acute myocardial
infarction (GUSTO III investigators) pag. 1118-1123
Min Sal 18/03/03 (2)
Notazioni di base
Trattamento di Riferimento (controllo attivo): R
Trattamento Sperimentale (nuovo): T
Efficacia: vera proporzione di risposte
positive, π
p: realizzazione empirica di
π
Min Sal 18/03/03 (3)
SCCR di Superiorità
Programmata per scoprire la differenza:
∗
T
R
π −π =δ
tra le medie vere dei pazienti trattati con T e quelli trattati
con R. Tale differenza è l’attesa giudicata importante
clinicamente per adottare un nuovo trattamento
Dimensione dello studio e analisi dei risultati
in termini di test di ipotesi secondo Neyman-Pearson
Min Sal 18/03/03 (4)
SCCR di Equivalenza (I)
Programmata per confermare l’assenza di una differenza
Margine di equivalenza clinica = ε , la più ampia
differenza, in valore assoluto, clinicamente accettabile, tra
gli effetti dei trattamenti così che una differenza maggiore
di questa avrebbe importanza sul piano pratico.
Dimensione dello studio e analisi dei risultati in termini di
intervallo di confidenza secondo Makuch e Simon (1978)
Min Sal 18/93/03 (5)
SCCR di Equivalenza (II)
di Equivalenza
Dimostrazione
Mancata dimostrazione
-ε
0
ε
Sull’asse delle ascisse è riportata la vera differenza tra gli effetti dei
due trattamenti a confronto (π R − π T )
Min Sal 18/03/03 (6)
SCCR di Non -Inferiorità (I)
Programmata per provare che T non è peggio di R.
Implica la definizione del margine di tolleranza ε
L’interesse del clinico è ora rivolto alla differenza in una
sola direzione.
Dimensione dello studio e analisi dei risultati in termini di
intervallo di confidenza.
Le SCCR di Non-Inferiorità sono più frequenti delle
SCRR di Equivalenza.
In Sl 18/03/03 (7)
SCCR di Non -Inferiorità (II)
Dimostrazione di Non- Inferiorità
Mancata dimostrazione
ε
0
Sull’asse delle ascisse è riportata la vera differenza tra gli effetti
dei due trattamenti a confronto π − π
(
Min Sal 18/03/03 (8)
R
T
)
Ipotesi nulle e Ipotesi alternative
SCCR di Superiorità:
Ηo :π R − πT ≥ 0
SCCR di Non-Inferiorità
Η
a
:π
−π
R
T
< 0
(Blackwelder, 1982)
Η 0 :π R − π T ≥ ε
Ηa :πR −πT < ε
Min Sal 18/03/03 (9)
ε
0
Regioni compatibili con le ipotesi
Superiorità clinica di T
π R −πT < 0
Tolleranza clinica
0 <π R −πT < ε
Min Sal 18/03/03 (10)
Esatta equiattività
π
R
− π
T
= 0
Inferiorità clinica di T
π R − πT ≥ ε
SCCR COBALT (I)
“…Thus besides offering the advantage of ease of use,
double bolus alteplase (T) might be at least as effective as
accelerated infusion (R) as used in the GUSTO I study.
The current trial was performed to test this hypothesis.”
Min Sal 18/03/03 (11)
SCCR COBALT (II)
End Points and Statistical Analysis
“The primary end point was death from all causes at 30
days.
The aim of the trial was to demonstrate therepeutic
equivalence.
Double bolus alteplase was to be considered at least
equivalent to accelerated infusion if the upper boundary
of the one sided 95 percent confidence interval of the
difference in 30 days mortality did not exceed 0 .40
percent point.”
ε = 0 . 004
Min Sal 18/03/03 (12)
SCCR COBALT (III)
“….0.40 per cent point. This value represented the lower
95 percent confidence limit of the 1 percent absolute
difference in 30-day mortality between an accelerated
infusion of alteplase and streptokinase that was observed
in GUSTO I Trial.”
Min Sal 18/03/03 (13)
SCCR COBALT (IV)
Da GUSTO I
nS = 20173 m S = 0 . 073
n A = 10344 m A = 0 . 063
p S = 0.927
p A = 0.937
SCCR di superiorità
p A − p S = 0 . 01
I.C. 95 % (0.0041 - - - - - - - -0.0159)
Min Sal 18/03/03 (14)
SCCR COBALT (V)
“We assumed a 30 day mortality rate of 6.3 percent with
accelerated infusion of alteplase , …, and a 30-day mortality
rate with double bolus alteplase of 5.4 percent.
The reduction in the 30 day mortality rate to 5.4 percent …”
fu ricavata dallo studio TIMI.
π t = (1 − 0.054 ) = 0.946
π R = (1 − 0.063) = 0.937
δ = π R − π t = − 0.009
Min Sal 18/03/03 (15)
SCCR COBALT (VI)
Dimensione della SCCR (Makuch e Simon, 1978 )
N = 2n = 2
Vincolo:
(z
1−α
+z
) [π (1 − π ) + π (1 − π )]
2
1− β
R
R
(ε − δ )
T
T
2
ε >δ
α = 0.05 (test 1 coda)
N=2 x 4029=8058
Min Sal 18/03/03 (16)
β = 0.20
SCCR COBALT (VII)
Curva di potenza
π R = 0 .9 3 7 , ε= 0 .0 0 4 , α = 0 .0 5 , β = 0 .2 0 , n = 4 0 2 9
1 .0 0
0 .9 0
0 .8 0
0 .7 0
1 -β
0 .6 0
0 .5 0
0 .4 0
0 .3 0
0 .2 0
0 .1 0
0 .0 0
-0 .0 3
-0 .0 2
-0 .0 1
0 .0 0
0 .0 1
0 .0 2
0 .0 3
πR - πT
Min Sal 18/03/03 (17)
SCCR COBALT (VIII)
Curva di potenza: alcuni punti
π
R
− π
-0.009
-0.0045
0.00
0.004
La scelta di
T
1 - β
0.80
0.481
0.182
0.05
π R − π T = −0.009
fu clinicamente
giustificata o servì solo a permettere il calcolo di N compatibile
con una dimensione dello studio economicamente accettabile?
Min Sa 18/03/03 (18)
SCCR COBALT (IX)
Riusultati
nR=3384
nT=3585
mR=0.0753
mT=0.0798
pR=0.9247
pT=0.9202
pR-pT=0.0045
(>ε)
L.S. I.C. 95%: 0.0149
Non - Inferiorità non dimostrata
Min Sal 18/03/03 (19)
SCCR COBALT (X)
End points and Statistical Analysis
Supporto razionale per la scelta del margine ε è fornita da:
“ …Thus if equivalence was demonstrated according to the
criterion (ε=0.004), superiority over streptokinase could be
claimed.”
Min Sal. 18/0303 (20)
Il documento ICH -10 suggerisce due criteri per la scelta
del margine ε:
•1. The detemination of the margin in a non-inferiority
trial is based on both statistical reasoning and clinical judgement,
and should reflect uncertainities in the evidence on which
the choice is based, and should be suitably conservative.
•2. This non inferiority margin cannot be greater than the
smallest effect size that the active drug would be reliably
expected to have compared with placebo in the setting of a
placebo-controlled trial
Min Sal 18/03/03 (21)
(a) 1. Historical Effect of Active Control versus Placebo is of a specified size
and there is belief that is maintened in the present trial (C>P)
Control
Placebo
(b) 2. Trial has the ability to recognizze the test drug within
non-inferiority margin ε of control
Placebo
0.8(C.-P.)
Test
Control
3. and Superior to a Placebo by a specified amount
Placebo
Min Sal. 18/03/03 (22)
Test
Control
Documento CPMP/EWP/482/99
Switching the objective of a trial from non-inferiority to
superiority is feaseble provided:
• The trial has been properly designed and carried out in
accordance with the strict requirement of a
non-inferirority trial.
• Actual p-values for the superiority are presented to
allow independent assessment of the strenght
of the evidence.
• Analysis according to the intention-to-treat principle
is given greatest emphasis
Min Sal 18/03/03 (23)
SCCR GUSTO III (I)
“…we tested the primary hypothesis that the mortality rate 30
days after acute infarction would be significantly lower with
reteplase than with alteplase.”
SCCR di superiorità: reteplase vs. alteplase
Min Sal. 18/03/03 (24)
SCCR GUSTO III (II)
Statistical Analysis
“The study design required the enrollment of 15,000
patients in order to have at least 85% power to detect
a 20% relative reduction in mortality with reteplase as
compared to alteplase.”
Min Sal. 18/03/03 (25)
SCCR GUSTO III (III)
Da GUSTO I:
π A = 0.063
δ = 0.063x 0.20= 0.0126
π R = 0.0504
α = 0.05 (due code)
N=15,000
Min Sal. 18/03/03 (26)
nel rapporto 2R:1A
β = 0.20
SCCR GUSTO III (IV)
Risultati
mR = 0.0747
pR=0.9253
mA = 0.0724
pA=0.9276
pA-pR=0.0023
I.C. 95% (-0.00657-----------0.01117)
Min Sal. 18/03/03 (27)
SCCR GUSTO III (V)
Discussion
“The chief finding of this trial is that reteplase is not
superior to alteplase for the treatment of acute myocardial
infarction.”
Affermazione assolutamente criticabile perché gli Autori
non predefinirono alcun margine ε di non- inferiorità
quando pianificarono la SCCR di superiorità.
Min Sal. 18/03/03 (28)
SCCR GUSTO III (VI)
Due scenari alternativi
1.ε = 0.004 da COBALT: la non inferiorità non sarebbe
provata
2.ε = 0.01
da GUSTO I (differenza tra alteplase e
streptokinasi): la non-inferiorità non sarebbe provata
dato che L.S. di I.C. 95% = 0.01117 > ε
Min Sal 18/03/03 (29)
Documento CPMP/EWP/482/99
Switching the objective of a trial from superiority to
non-inferiority may be feaseble provided:
•The non- inferiority margin with respect to the control
treatment was pre-defined or can be justified. (The
latter is likely to prove difficult and to be limited
to rare cases where there is a widely accepted value for ε).
•Analysis according to the intention-to-treat principle
and PP analysis, showing confidence intervals and p-values
for the null hypothesis of inferiority, give similar findings.
Min Sal. 18/03/03 (30)
Documento CPMP/EWP/482/99 (ctd)
•The trial was properly designed and carried out in
accordance with the strict requirements of a
non- inferiority trial (see ICH-E9 and E10)
•The sensitivity of the trial is high enough to ensure that it
is capable of detecting relevant differences if they exist.
•There is direct or indirect evidence that the control
treatment is showing its usual level of efficacy.
Min Sal. 18/03/03 (31)
Methodological Attribute
Studies Adhering to Attribute
(n=88), n (%)
Statement of research aim
Aim of equivalence
Proportionate difference between entities being
compared < 20%
Quantitative boundary chosen
Method of stochastic testing
Not done
Failed test for superiority
Tested for equivalence
Required sample size calcluated
50 (57)
45 (51)
51 (58)
20 (23)
9 (10)
59 (67)
20 (23)
29 (33)
W.L. Greene, J. Concato, A.R. Feinstein, Ann.of Inter. Med. 2000; 132:715-722
Min Sal 18/03/03 32
prof. Grigoletto
Direzione Generale della Valutazione dei Medicinali e Farmacovigilanza
Workshop
Le competenze biostatistiche nell’ambito dei Comitati Etici locali
Francesco Grigoletto
Università degli Studi di Padova
________________________
Società Italiana di Statistica Medica ed Epidemiologia Clinica
Istituto Superiore di Sanità
Roma, 18 marzo 2003
Comitato Etico per la Sperimentazione Clinica
Azienda Ospedaliera di Padova
Attività 1999-2002
Protocolli Esaminati
N=602
Area Oncologica
N=84
Fase II
N=47
Campione prefissato
N=7
Area Non Oncologica
N=518
Fase III
N=35
Multistadio
N=40
Gehan (N=3)
Fleming (N=6)
Simon (N=27)
Chen (N=4)
Campione prefissato
N=35
Altro
N=2
Gruppi sequenziali
N=2
Pocock (N=2)
Caratteristiche dei Disegni di Fase II
(qui considerati)
• Obiettivo: Determinare se una nuova procedura di
trattamento ha una efficacia sufficiente per
procedere ad una valutazione comparativa
di Fase III
• Bracci:
Gruppo singolo
• End point: Risposta (completa e/o parziale) al
trattamento (variabile dicotomica)
• Follow-up: Breve (generalmente inferiore ai 24 mesi)
Problema di verifica d’ipotesi
• Ipotesi:
H0: π ≤ p0
H1: π ≥ p1
essendo:
π =
p0 =
p1 =
Proporzione di risposte (successi) in
una definita popolazione
Tasso massimo di inefficacia
Tasso minimo di efficacia
• Vincoli per i rischi di errore:
Pr(Rifiuto di H0H0) ≤ α
Pr(Rifiuto di H1H1) ≤ β
Disegni possibili
• Numerosità predefinita
• Sequenziale (con analisi dopo ogni paziente completato)
• Multistadio (sequenziale per gruppi, con analisi dopo
completamento di ciascun gruppo)
Disegni Multistadio
• Vantaggi:
– Numerosità campionaria attesa inferiore
(a parità di rischi di errore)
– Riduzione tendenziale dell’esposizione ad
un trattamento inefficace
– Tendenziale disponibilità anticipata di un
trattamento efficace
• Svantaggi:
– Maggiore complessità formale
– Maggiore impegno di gestione
Struttura dei Disegni Multistadio
• Numero massimo di stadi:
K
• Unità incluse ad ogni stadio:
n1, n2,.…., nK
• Valori soglia di accettazione:
A1, A2,….., AK
• Valori soglia di rifiuto:
R1, R2,….., RK (Ai < Ri)
• Decisione allo stadio imo basata sul numero di successi Si:
− Si rifiuta H0 se
Si ≥ Ri
− Si accetta H0 se
Si ≤ Ai
− Si passa allo stadio successivo altrimenti
Disegno di Gehan (1961)
• Due stadi (n1, n2)
• Al primo stadio:
– si accetta H0 se S1/n1 = 0
– altrimenti, si passa al secondo stadio
(non si può concludere per l’efficacia)
• n1 si ottiene dall’equazione:
β1= Pr(S1=0p1) = (1- p1)n1
(n1 =14 per p1=0,20 e β1=5%)
• Secondo stadio finalizzato alla stima di π, specificandone la
precisione (non occorre prefissare p0 e α)
• Tavole di numerosità in Gehan (1961) e Machin et al(1996)
Disegno di Fleming (1982)
• Numero di stadi K arbitrario (generalmente 2 o 3)
• Parametri da specificare: K, p0, p1, α, β
• Numerosità totale N calcolata come nello stadio singolo
• N è ripartito tra i K stadi arbitrariamente (in genere
equamente)
• Ciascuno stadio permette di accettare l’ipotesi di inefficacia
(π ≤ p0) o l’ipotesi di efficacia (π ≥ p1)
• All’ultimo stadio il test fa riferimento ad un unico valore
critico definito in funzione di α e β
• Numerosità per varie combinazioni dei parametri in Fleming
(1982)
Disegni di Simon (1989)
• Numero di stadi K= 2
• 1 - Disegno ottimo: minimizza il numero medio di pazienti
(ASN) quando π=p0 per prefissati rischi di errore α e β
• 2 - Disegno minimax: minimizza la numerosità massima
richiesta (n=n1+n2) che soddisfa i rischi α e β quando π=p0
• Numero di pazienti a ciascuno stadio determinata dai vincoli di
minimizzazione
• Interruzione anticipata al primo stadio solo per inefficacia
(talora dopo troppo lunghe sequenze di insuccessi)
• Numerosità derivate utilizzando la binomiale per alcune
combinazioni dei parametri in Simon (1989) e tramite il
pacchetto PASS 2000 (J. Hintze, Kaysville, Ut) con metodo
iterativo
Esempio di disegni ottimale e minimaxa
(Simon 1989)
Rifiuta il farmaco se il
tasso di risposta
a
Disegno
≤r1/n1
≤r/n
ASN(p0)
PET(p0)
Ottimale
1/10
5/29
15,0
0,74
Minimax
1/15
5/25
19,5
0,55
Per entrambi si è posto: p0=0,10, p1=0,30, α = 0,05, β =0,20.
PET = Probability of Early Termination
Output di PASS per un disegno a due stadi con i
parametri specificati (1)
Two-Stage Clinical Trials Sample Size
Page/Date/Time
1 13/03/03 19.12.19
Possible Designs For P0=0.050, P1=0.250, Alpha=0.050, Beta=0.200
N1 R1
PET
N
R
Ave N Alpha Beta
Constraints
Satisfied
16 2
0.000
16
2
16.00
0.043
0.197
Single Stage
12 0
0.540
16
2
13.84
0.043
0.199
Minimax
9
0.630
17
2
11.96
0.047
0.188
Optimum
0
Output di PASS per un disegno a due stadi con i
parametri specificati (2)
Two-Stage Clinical Trials Sample Size
Possible Designs For P0=0,100, P1=0,250, Alpha=0,050, Beta=0,100
N1
R1
PET
N
R
Ave N Alpha Beta
Constraints
Satisfied
55
9
0,000
55
9
55,00
0,044
0,089
Single Stage
31
3
0,624
55
9
40,03
0,042
0,099
Minimax
28
3
0,695
57
9
36,86
0,048
0,099
Optimum
Disegno di Chen (1997)
• Estende a K=3 stadi i disegni di Simon (ottimale e minimax)
• Supera il problema di trattare un numero “eccessivo” di
pazienti con un farmaco inefficace
• Riduce la numerosità campionaria media di circa il 10% (non
sempre)
• Numerosità:
–
–
–
riportate in Chen (1989) per alcuni parametri
programma in FORTRAN disponibile dall’autore
PASS, versione 2003
Three-Stage Phase II Sample Size
Page/Date/Time
1 13/03/03 19.12.19
Possible Designs For P0=0.500, P1=0.700, Alpha=0.050, Beta=0.200
Stage 1 Stage 2 Stage 3
R1/N1
R2/N2
R3/N3 Ave N
23/37
23/37
23/37
37.00
7/16
13/25
23/37
25.31
6/13
13/24
24/39
22.12
Stage 1
Pet P0
0,000
0.402
0.500
Overall Beta
Pet P0 Alpha
0.000
0.049
0.673
0.048
0.759
0.048
Constraints
Beta
Satisfied
0.193 Single Stage
0.199
Minimax
0.199
Optimum
Report Definitions
N1 is the sample size in the first stage.
R1 is the drug rejection number in the first stage.
N2 is the sample size in the first and second stages.
R2 is the drug rejection number in the second stage.
N3 is the combined sample size of all three stages.
R3 is the drug rejection number in the third stage.
Stage 1 PET P0 is the probability of early termination at the first stage.
Stage 2 PET P0 is the probability of early termination at the second stage.
AVE N is the average sample size if this design is repeated many times.
Alpha is the probability of rejecting that P<=P0 when this is true.
Beta is the probability of rejecting that P>=P1 when this is true.
P0 is the response proportion of a poor drug.
P1 is the response proportion of a good drug.
Comitato Etico: Aspetti da verificare in un
protocollo oncologico di Fase II
• Chiara definizione degli endpoint che indicano successo
• Indicazione del disegno utilizzato (con riferimento
bibliografico) e sua giustificazione
• Specificazione di tutti i parametri necessari a caratterizzare
il disegno (in particolare p0 e p1) e loro giustificazione
• Numerosità campionaria dei singoli stadi
• Inclusione di stime intervallari nelle analisi
• Impegno ad informare il CE sui risultati intermedi
Sperimentazioni cliniche comparative di Fase III:
Analisi intermedie (disegni con gruppi sequenziali)
• Scopo:
cautelarsi verso inattese e rimarchevoli
differenze tra i gruppi di trattamento
• Disegno:
numerosità prefissate dei gruppi di
trattamento, con analisi intermedie per saggiare
l’eventuale opportunità di una conclusione
anticipata dello studio
Giustificazione in termini di:
• Eticità:
tendenzialmente riduce l’esposizione a un
trattamento inferiore o anticipa la
disponibilità di un trattamento superiore
• Risparmio: di tempo e risorse
• Qualità:
può evidenziare e rimediare procedure scorrette
Trial oncologici di Fase III:
Analisi intermedie di gruppi sequenziali
• Endpoint:
sopravvivenza (vari tipi)
• Problema 1: una analisi anticipata di sopravvivenza può
non assicurare la sensibilità dei disegni a
campione prefissato verso differenze tra
trattamenti che si manifestano alla fine del
periodo di follow-up
• Problema 2: aumentando il numero di verifiche, aumenta
la probabilità di evidenziare una significatività
statistica, anche se è vera H0 (si devono
perciò aggiustare i rischi di errore)
Valori critici ed errore di I tipo
• Numero di analisi intermedie (looks): K
• Tempi di verifica: ti
(i=1,…, K)
• Funzioni test calcolate sui dati cumulati: zi
• Valori critici bi
(i=1,…, K)
(i=1,…, K) tali che:
α =Pr(rifiutare H0H0) =
= PrH0(zt1 ≥b1 ∪ zt2≥b2 ∪………. ∪ ztK≥bk)
Strategia di definizione dei valori critici:
(Pocock 1977)
• Tempi equispaziati, ossia ti = i/K (i=1,…, K)
• Valori critici
bi = c
(i=1,…, K)
tali che:
PrH0(zt1≥c ∪ zt2≥c ∪….. ∪ ztk≥c) = α
(Haybittle-Peto 1976)
• Valori critici
bi = c
(i=1,…, K-1)
tali che:
PrH0(zt1≥c ∪ zt2≥c ∪….. ∪ ztk≥z1-α) = α
(O’Brien-Fleming 1979)
• Valori critici
bi = c
(i=1,…, K)
tali che:
PrH0(zt1
t1
≥c ∪ zt2
t2
≥c ∪….. ∪ ztk
tk
≥c) = α
Valori normali standardizzati e corrispondenti
livelli nominali di significatività per tre test
sequenziali (K=5, α=5%, 2 code)
Disegno
O’Brien-Fleming Haybittle-Peto
Stadio
Pocock
1
2,413
0,016
4,562
0,00001
3,290
0,001
2
2,413
0,016
3,226
0,001
3,290
0,001
3
2,413
0,016
2,634
0,008
3,290
0,001
4
2,413
0,016
2,281
0,023
3,290
0,001
5
2,413
0,016
2,040
0,041
1,967
0,049
Funzione d’uso (spending function) di α a tempi
qualsiasi
(Lan e DeMets, 1983)
t=
frazione di tempo sulla durata complessiva del trial
frazione di pazienti sul totale
• Funzione di α in t tale che: α(0) = 0
α(1) = 1
• Possibili funzioni: α(t) = 2-2Φ(zα/2/
)
t t]
α(t) = α ln [1+(e-1)
α(t) = α
(O’Brien-Fleming)
(Pocock)
t3/2
• Si calcolano quindi i valori critici corrispondenti ad α(t)
• Metodo flessibile (non occorre pre-specificare i tempi e il numero
di analisi)
Group Sequential - Survival
Numeric Results for Two-Sided Logrank Test
(Assuming Exponential Survival)
Total
Total
Sample Required
Power
Size (N) Events Alpha
0,901100
236
142
0,0500
Proportion
Beta
Surv. (S1) Surv. (S2)
0,098900 0,3000 0,5000
Details when Spending = O'Brien-Fleming, N = 236, d = 142,
S1 = 0,3000, S2 = 0,5000
Look
1
2
3
4
5
Time
0,2000
0,4000
0,6000
0,8000
1,0000
Lower
Bndry
-4,87688
-3,35695
-2,68026
-2,28979
-2,03100
Upper
Bndry
4,87688
3,35695
2,68026
2,28979
2,03100
Nominal
Alpha
0,000001
0,000788
0,007357
0,022034
0,042255
Drift 3,28512
Group Sequential - Survival
Page/Date/Time
3 14/03/03 17.58.49
O'Brien-Fleming Boundaries with Alpha = 0,05
5
4
3
Z Value
2
Upper
1
0
1
2
3
4
5
-1
Lower
-2
-3
-4
-5
Look
Power
0,000328
0,100414
0,448228
0,747752
0,901100
Comitato Etico: Aspetti da verificare in un
protocollo oncologico di Fase III
• Chiara definizione degli endpoint (in particolare le condizioni
di sopravvivenza previste)
• Indicazione di eventuali analisi intermedie di qualsiasi tipo
pianificate (FDA Guidelines)
• Se sono previste analisi intermedie equispaziate nel
tempo, specificazione di:
- numero K
- tempi di osservazione
- distribuzione di α
• Se sono comunque previste analisi intermedie,
specificazione della funzione d’uso di α
• Inclusione di stime intervallari nelle analisi di sopravvivenza
pianificate
Aggiustamenti dei livelli di
significatività e di confidenza:
necessità e opportunità
CPMP – Points to consider on multiplicity
issues in clinical trials
ICH Harmonised Tripartite Guideline –
Statistical principles for clinical trials
dr. Cesana
Le competenze biostatistiche
nell’ambito dei Comitati etici locali
Aggiustamenti dei livelli di significatività e di
confidenza: necessità ed opportunità
Dr. Bruno Mario CESANA
Ospedale Maggiore di Milano, IRCCS
Direzione Scientifica - Laboratorio Epidemiologico
e-mail: [email protected]
Roma 18 Marzo 2003
Osservatorio Nazionale Sperimentazione Clinica
ISS / SISMEC
“International Conference on Harmonization (ICH) E9 Guideline:
STATISTICAL PRINCIPLES for CLINICAL TRIALS”.
Expert Working Group (1,2) da: “Committee for Proprietary
Medicinal Products (CPMP) Guideline – Biostatistical
Methodology in Clinical Trials in Applications for Marketing
Authorisations for Medicinal Products, December 1994" (2,3).
•FDA (USA): Guideline for the Format and Content of the Clinical
and Statistical Sections of a New Drug Application, July 1988).
•Japanese Ministry of Health and Welfare: (Guidelines on the
Statistical Analysis of Clinical Studies, March 1992).
La ICH E9 adottata dal CPMP (marzo 1998) è divenuta
operativa in Europa.
E’ poi stata adottata negli USA ed in Giappone.
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“The statistical principles outlined in the ICH E9 Guideline are
scientifically correct and should be adhere to by both
European Member States and companies” (5).
“…adherence to the guideline is a must for companies…” (5).
“To prevent delay in receiving marketing authorizations, it is
strongly recommended that attention be paid to the statistical
principles outlined in ICH E9 when designing new clinical
development programmes” (5).
“…analyses presented allow an assessment of the drug’s risk /
benefit to be carried out, part of this being that a good estimate
of the size of the potential benefit is available (6).
ICH E9 provides statistical advice that if followed can help
to ensure that this is the case” (6).
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Lo “Statistical Analysis Plan” a cui la ICH E9 fa esplicito riferimento:
paragraph V: Data Analysis Considerations
section: 5.1 “Prespecification of the Analysis
deve essere compilato in accordo ai suggerimenti della ICH E9.
Glossary: a statistical analysis plan is a document that contains a
more technical and detailed elaboration of the principal features of
the analysis described in the protocol, and includes detailed
procedures for executing the statistical analysis of the primary
and secondary variables and other data.
“… the final statistical model based on a formal blind review”.
Una specifica sezione della ICH E9 fa riferimento a:
ICH E9 – 5.6: Adjustment of Significance and Confidence Levels.
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La ICH E9 sostiene l’approccio frequentista di Neyman e Pearson
(1928, 1933).
“When multiplicity is present, the usual frequentist approach to the
analysis of clinical trial data may necessitate an adjustment to the type I
error”.
Glossary: frequentist methods: statistical methods, such as
significance tests and confidence intervals, which can be
interpreted in terms of the frequency of certain outcomes occurring in
hypothetical repeated realisations of the same experimental situation.
Per Neyman e Pearson (1928, 1933) nel contesto di una formulazione
che fa riferimento ad una ipotetica infinita popolazione di esperimenti
corrispondente a ripetizioni dell’esperimento effettivamente effettuato,
si delineano le nozioni di probabilità di errore di I tipo (la soglia di
significatività α) e di II tipo (β) come rispettivamente le probabilità di
rifiutare (non accettare) vere H0 e di non rifiutare (accettare) false H0,
facendo riferimento alla distribuzione generata dalla suddetta ipotetica
infinita popolazione di esperimenti data Ha.
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TEORIA DEL TEST DI SIGNIFICATIVITÀ:
Ipotesi nulla: H0 ed ipotesi alternativa: Ha.
La pertinente distribuzione di probabilità della statistica test, dati gli
assunti distribuzionali (esatti o asintotici),
Calcolo della probabilità, data H0, di ottenere un valore pari o
superiore (inferiore) a quello calcolato (livello di significatività, p),
Soglia del livello di significatività (α): per p ≤ α si può essere
fiduciosi di aver ottenuto una sufficiente evidenza a rifiutare (non
accettare) H0 per sostenere Ha; dati i valori sperimentali, H0 diviene
talmente poco probabilisticamente plausibile da essere
considerata “non plausibile o non ulteriormente sostenibile”.
La potenza (1-β) di un esperimento ad evidenziare un prefissato effetto
è il valore di probabilità a cui H0 dovrebbe essere rifiutata.
Per – Comparison Error Rate (PCE): la proporzione attesa di errori di
I tipo.
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ICH E9: section: 5.5 estimation, confidence intervals
and hypothesis testing.
It is important to clarify whether one- or two-sided tests
of statistical significance will be used, and in particular to
justify prospectively the use of one-sided tests.
The approach of setting type I errors for one-sided tests at
half the conventional type I error used in two-sided tests
is preferable in regulatory settings.
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DEFINIZIONI: livello di confidenza
Il calcolo di una stima intervallare del parametro di interesse:
(θ: differenza tra il nuovo trattamento e lo standard e/o il placebo).
Si richiede che la probabilità che il parametro di interesse (θ) sia
compreso tra il limite inferiore (L0) ed il limite superiore (L1) sia pari
a 1-α, essendo 1-α il coefficiente di confidenza:
Pr (L0 ≤ θ ≤L1) = 1 - α
Da questa formulazione che porta ad affermare che il parametro di
interesse è compreso nell’intervallo con probabilità 1-α, in accordo
all’approccio frequentista di Neyman e Pearson, in cui il parametro è
una costante e gli intervalli sono essi stessi variabili casuali
(analogamente al limite superiore ed inferiore), si conclude che
l’affermazione che il parametro è compreso nei limiti calcolati ha
la probabilità di essere corretta pari a 1-α e quindi nel 100(1-α)%
dei casi nell’ambito di una serie ripetuta di esperimenti (in the long
run).
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ICH E9 Section 5.6: Multiplicity may arise, for example,
from:
-multiple primary variables (see section 2.2.2),
-multiple comparisons of treatments,
-repeated evaluation over time and/or interim analyses
(see section 4.5).
Methods to avoid or reduce multiplicity are sometimes
preferable when available, such as:
-the identification of the key primary variable (multiple
variables),
-the choice of a critical treatment contrast (multiple
comparisons),
-the use of a summary measure such as ‘area under the
curve’ (repeated measures).
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In confirmatory analyses, any aspects of multiplicity
which remain after steps of this kind have been taken
should be identified in the protocol; adjustment should
always be considered and the details of any adjustment
procedure or an explanation of why adjustment is not
thought to be necessary should be set out in the analysis
plan.
Paragrafo 1.2 scope and direction:
•The focus of this guidance is on statistical principles.
•It does not address the use of specific statistical
procedures or methods.
•Specific procedural steps to ensure that principles are
implemented properly are the responsibility of the
sponsor.
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… the actual responsibility for all the statistical work associated with
clinical trials will lie with an appropriately qualified and experienced
statistician, as indicated in ICH E6…(guideline for good clinical
practice, 1996).
For each clinical trial contributing to a marketing application, all
important details of its design and conduct and the principal
features of its proposed statistical analysis should be clearly
specified in a protocol written before the trial begins.
The extent to which the procedures in the protocol are followed and
the primary analysis is planned a priori will contribute to the degree
of confidence in the final results and conclusions of the trial.
The principles outlined in this guidance are primary relevant to
clinical trials conducted in the later phases of development, many
of which are confirmatory trials of efficacy.
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Section 2.2.2 primary and secondary variables
Primary variable (‘target’ variable, primary endpoint)
should be the variable capable of providing the most
clinically relevant and convincing evidence directly
related to the primary objective of the trial.
There should generally only one primary variable.
(the primary variable should generally be the one used
when estimating the sample size).
To avoid multiplicity concerns arising from post hoc
definitions, it is critical to specify in the protocol the
precise definition of the primary variable as it will be
used in the statistical analysis.
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Section: 2.2.3 composite variables
Multiple measurements associated with the primary
objective …. to integrate or combine ….into a single or
‘composite’ variable, using a pre-defined algorithm.
This approach addresses the multiplicity problem
without requiring adjustment to the type I error.
Mortalità, recidiva di infarto miocardico,
congestizia, disfunzione del ventricolo sn.
Overall mortality, graft + patient survival.
(analisi per singolo end-point).
insufficienza
cardiaca
Considerazioni (Cannon, 1997, (7), Gent, 1997, (8) ):
-statistiche: bassa incidenza – potenza dello studio,
-patofisiologiche: stessa malattia di base,
-clinico – terapeutiche: effetto “globale” della terapia.
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Section: 2.2.4 global assessment variables
Glossary: a single variable, usually a scale of ordered
categorical ratings, which integrates objective variables
and the investigator’s overall impression about the state
or change in state of a subject.
CGI: Clinical Global Improvement.
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Section: 2.2.5 multiple primary variables
It may sometimes be desirable to use more than one primary
variable, each of which (or a subset of which) could be sufficient
to cover the range of effects of the therapies.
The effect on the type I error should be explained because
of the potential for multiplicity problems (see section 5.6); the
method of controlling type I error should be given in the
protocol.
The extent of intercorrelation among the proposed primary
variables may be considered in evaluating the impact on type I
error.
If the purpose of the trial is to demonstrate effects on all of the
designated primary variables, then there is no need for
adjustment of the type I error, but the impact on type II error
and sample size should be carefully considered.
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Section: 4.5 interim analysis and early stopping
Glossary: interim analysis: any analysis intended to compare
treatment arms with respect to efficacy or safety at any time prior
to the formal completion of a trial.
When an interim analysis is planned with the intention of
deciding whether or not terminate a trial, this is usually
accomplished by the use of a group sequential design which
employs statistical monitoring schemes as guidelines (see
section 3.4: group sequential designs).
The protocol should describe the schedule of interim analyses,
or at least the considerations which will govern its generation,
for example if flexible alpha spending function approaches
are to be employed.
The stopping guidelines and their properties should be
clearly described in the protocol or amendments.
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One-day Discussion Forum (London, October 1998)
arranged by Statisticians in the Pharmaceutical Industry
(PSI), a United Kingdom-based Professional Association of
Statisticians Interested in
Pharmaceutical Industry (9).
Workshop 4:
multiplicity.
the application of
Statistics in the
primary / secondary endpoints and
Q3: what are the preferred methods of adjustment for multiple
endpoints ?.
-Bonferroni and hierarchical procedures.
Q1: what are the preferred strategies for handling multiple treatment
comparisons ?.
-use of closed test procedures,
-testing the overall treatment effect before considering any individual
comparisons,
-use of Dunnett’ s (10) procedure, and
-use of Bonferroni correction.
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Points to Consider on Multiplicity Issues in Clinical
Trials (CPMP/EWP/908/99).
(Adoption by CPMP – September 2002)
Multiplicity of inferences is present in virtually all
clinical trials.
α = 0.025; five subgroups: 1 - (1- α)5 = 1 - 0.88 = 0.12 o
12%.
“If statistical tests are performed on five subgroups,
independently of each other and each at a significance
level of 2.5% (one sided directional hypotheses), the
chance of finding at least one false positive statistically
significant test increases to 12%.”
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“Control of the study-wise rate of false positive
conclusions at an acceptable level α is an important
principle…”.
“Throughout this document the term “control of type I
error“ rate will be used as an abbreviation for the control of
the family-wise type I error in the strong sense, i.e.,
there is control on the probability to reject at least one true
null hypothesis, regardless which subset of null hypotheses
happens to be true.”
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2)-Adjustment of multiplicity – when is it necessary and when is it not ?.
3)-How to interpret significance with respect to multiple secondary
variables and when can a claim be based on one of these ?.
4)-Reliable conclusions be drawn from a subgroup analysis, and
restriction of the licence to a subgroup.
5)-How should one interpret the analysis of “responders” in conjunction
with the raw variables ?.
6)-How should composite endpoints be handled statistically with
respect to regulatory claims ?.
A separate document may cover interim analyses or stepwise
designed studies.
“Potential multiplicity issues concerning the analysis of repeated
measurements are not considered in this document (summary
measures, pre-specified endpoint).” [trend analysis].
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2)- Adjustment of multiplicity – when is it necessary and
when is it not ?.
“No adjustment of the type I error:
a study with two treatment groups, with a single primary
variable and with a confirmatory statistical strategy that prespecifies just one single null hypothesis relating to the
primary variable and no interim analysis”.
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“For the purpose of estimation and for the appraisal of the precision
of estimates, confidence intervals are of paramount importance but
methods for their construction that are consistent with the tests are
not available for many of the more complex multiple-level-α-tests (or
more generally closed tests) aiming at controlling the type I error”.
“Because alternative methods to deal correctly with multiplicity are
often available which may lead to different conclusions, predefinition of the preferred multiple-level-α-test is necessary”.
“To avoid problems in interpretation, details of the procedure should
be contained in the study protocol or the statistical analysis plan”.
“If a multiple test situation occurs which has not foreseen, a
conservative approach will be necessary e.g. Bonferroni’s or a related
procedure. Inherently there will be a loss of power.”
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2.1-Multiple primary variables – when no formal adjustment
is needed:
2.1.1-Two or more primary variables are needed to
describe clinically relevant treatment benefits.
“Statistical significance is needed for all primary variables.
therefore, no formal adjustment is necessary”.
2.1.2-Two or more primary variables ranked according to
clinical relevance
“No formal adjustment is necessary. However, no confirmatory
claims can be based on variables that have a rank lower than or
equal to that variable whose null hypothesis was the first that
could not be rejected”.
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2.2-Analysis sets
“In general, multiple analyses on varying subsets of
subjects or with varying measurements for the purpose
of investigating the sensitivity of the conclusions drawn
from the primary analysis should not be subjected to
adjustment for type I error”.
2.3 - Alternative statistical methods – multiplicity concerns
2.4 - Multiplicity in safety variables
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2.5 - Multiplicity concerns in studies with more than two
treatment arms.
“The discussion is limited to the more common and simple
designs.
As a general rule it can be stated that control of the
family-wise type I error in the strong sense (i.e.
application of closed test procedures) is a minimal
prerequisite for confirmatory claims”.
“It should be remembered that the usual confidence
intervals for the pairwise differences between
treatment groups are – except for a few instances – not
consistent with the closed testing procedures, and are
usually too narrow.”
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2.5.1 - The three arm “gold standard” design
-the reference drug, -placebo and -the investigational drug.
Usually the aims of such a study are manifold:
(1)–to demonstrate superiority of the investigational drug over placebo
(proof of efficacy);
(2)–to demonstrate superiority of the reference drug over placebo
(proof of assay sensitivity, see ICH E10 section 2.5.1.1.1);
(3)–to demonstrate that the investigational drug retains most of the
efficacy of the reference drug as compared to placebo (proof
of non-inferiority).
“If all of these are objectives, all three comparisons must show
statistical significance at the required level, and no formal
adjustment is necessary”.
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AGGIUSTARE O NO ?
•CONTRASTI TRA DUE TRATTAMENTI ATTIVI VERSO IL
PLACEBO
•CONTRASTI TRA PIÙ TRATTAMENTI.
Perché se si confronta A vs. B in un trial e A vs. C in un
altro trial è assolutamente non discutibile che la soglia di
significatività sia pari ad α in entrambi in casi e se
invece si confronta A vs. B e A vs. C in un solo trial si
dovrebbe aggiustare ?.
Trial con tre trattamenti per una stima più precisa della
variabilità e per arruolare un minor numero di pazienti.
(Proschan - Waclawiw, 2000).
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2.5.2 - Proof of efficacy for a fixed combination
“CPMP/EWP/240/95 requires that “each substance of a fixed
combination must have documented contribution within the
combination”.
For a combination with two (mono) components, a 3-arm study.
“No formal adjustment of the overall significance level is
necessary, because both pairwise comparisons must show
statistically significant superiority”..
Multiple-dose factorial designs are employed for the assessment of
combination drugs for the purpose:
(1)-to provide confirmatory evidence that the combination is more
effective than either component alone (CPMP/ICH/387/95 – ICH E4) .
This is usually achieved using global test strategies.
(2)-to identify an effective and safe dose combination (or a range of
useful dose combinations).
Appropriate closed test procedures.
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2.5.3 - Dose-response studies (NO STAtistical Significance
Of Trend dose).
“The control of the family-wise type I error in the
strong sense is mandatory.”
“Specific recommendations are beyond the scope of this
document”.
“There are various methods published in the relevant literature on
closed test procedures with relevance to multiple dose studies that
can be adapted to the specific aims and that provide the necessary
control on the type I error.” (Bauer: 1997, 1998, Rom: 1994).
“...Sometimes a study ... is successful at demonstrating an overall
positive correlation of the clinical effect with increasing dose….”
“Estimates and confidence intervals from pairwise comparisons of
single doses are then used in an exploratory manner for the
planning of future studies.” In this case, an adjustment of the
type I error is not necessary.
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3)-How to interpret significance with respect to multiple
secondary variables and when can a claim be based on
one of these ?.
Un “endpoint secondario” permette di ottenere ulteriori
caratterizzazioni cliniche dell’effetto del trattamento ma
non è sufficiente a caratterizzarne completamente il
beneficio o a supportare una richiesta di registrazione del
trattamento (O’Neill, 1997).
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3.1 Variables expressing supportive evidence:
No claims are intended; confidence intervals and
statistical tests are of exploratory nature.
3.2 Secondary variables which may become the basis for
additional claims (after the primary objective of the clinical trial has
been achieved…. A valid procedure, to deal with this kind of secondary
variable is to proceed hierarchically).
3.3 Variables indicative of clinical benefit
If not defined as primary variables, clinically very important variables (e.g.
mortality) need further study when significant benefits are observed, but the
primary objective has not been achieved (O’NEILL, 1997).
Moyé (2000): Prospective Alpha Allocation Scheme (PAAS: αE (0.10), αP, αS)
SURROGATE ENDPOINTS ?
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4. Reliable conclusions from a subgroup analysis, and
restriction of the licence to a subgroup.
Reliable conclusions from subgroup analyses generally
require pre-specification and appropriate statistical
analysis strategies.
A licence may be restricted if unexplained strong
heterogeneity is found in important sub-populations, or if
heterogeneity of the treatment effect can reasonably be
assumed but cannot be sufficiently evaluated for important
sub-populations.
The evaluation of uniformity of treatment effects
across subgroups is a general regulatory concern.
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Multiple Comparisons Procedures (MCP)
Uno dei più importanti e controversi aspetti delle MCP:
la scelta della famiglia di inferenze (“family”).
•costituiscono una naturale e coerente unità: “contextual
relatedness”.
•sono considerate contemporaneamente nel processo
decisionale (è importante considerare il rischio d’errore).
•principio di parsimonia: minor numero possibile.
{D vs. P}, {C1 vs. P, C2 vs. P}, {D vs. C1, D vs. C2}
Cook & Dunnett (1998):
{D vs. P, C vs. P}, {D vs. C}
D’Agostino (1993)
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Familywise error rate (FWE) è definita come la probabilità
di fare una o più false positive conclusioni per tutte le
ipotesi test in una particolare famiglia.
FWE = P(rifiutare almeno una delle H01, H02,…,H0k | H01,
H02,…,H0k sono tutte vere).
Controllare FWE è appropriato se non si può accettare
alcun errore di I tipo nella famiglia, indipendentemente da
quante delle k ipotesi nulle sono vere.
Le procedure di confronti multipli (MCP) controllano la FWE
ad α:
Pr(almeno una vera ipotesi nulla è falsamente rifiutata) ≤ α.
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“controllo debole” della FWE ad α è ottenuto se questa
probabilità di errore di I tipo è garantita essere al massimo
α quando tutte le ipotesi nulle sono vere.
Ad esempio la “protected Least Significance Difference
(LSD)” procedura di Fisher (1935).
Procedura a due stadi:
1)-la globale H0 è saggiata mediante un test F al livello α,
2)-in caso di rifiuto, tutte le differenze a coppie sono
saggiate mediante un test t di Student al livello α.
Si effettuano test t multipli ciascuno a livello α solo se un
preliminare test F è statisticamente significativo a livello α.
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“controllo forte” della FWE ad α è ottenuto se la
probabilità di effettuare almeno un errore di I tipo su
tutte le ipotesi test della famiglia è al massimo α,
indipendentemente da quante ipotesi nulle della famiglia
possano essere vere.
La seconda procedura di Fisher (1935):
“procedura di Bonferroni” ad un singolo stadio.
Si effettuano test t multipli (confronti a coppie di k medie,
ad esempio) ciascuno al livello α* = α /  k  , senza un
 2
 
preliminare test F.
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CLOSURE PRINCIPLE / METHOD (PERITZ, 1970).
CLOSED TESTING METHODS / PROCEDURES
“controllo forte” della FWE ad α (errore di I e III Tipo).
“closed under intersection” = se H01, H02 ∈ I,
allora H01 ∩ H02 ∈ I.
Siano date quattro (k) medie (k(k-1)/2 confronti a coppie).
Si ottiene la “chiusura” della famiglia costituendo i seguenti
set non vuoti di ipotesi:
1)-H1234: µ1 = µ2 = µ3 = µ4
2)-H123: µ1=µ2= µ3; H124: µ1= µ2= µ4; H134: µ1=µ3=µ4; H234: µ2 = µ3 = µ4
3)-H(12)∩(34): µ1=µ2∩µ3=µ4; H(13)∩(24): µ1=µ3∩µ2=µ4;H(14)∩(23): µ1=µ4∩µ2=µ4;
4)-H12: µ1=µ2; H13: µ1=µ3; H14: µ1=µ4; H23: µ2=µ3; H24: µ2=µ4; H34: µ3=µ4;
Ognuna di queste ipotesi è saggiata a α [ (3)- a 1-(1-α)1/2, Peritz, 1970].
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Un’ipotesi elementare (minimale), diciamo H12: µ1=µ2, è
dichiarata statisticamente significativa, se risultano
statisticamente significativi anche i test di significatività che
saggiano le ipotesi: H1234, H123, H124, e H(12)∩(34) che
comprendono H12.
Test di significatività applicati gerarchicamente solo a (set di) ipotesi
compresi in set di ipotesi di ordine superiore che sono state rifiutate:
si saggia H123, H124, e H234 solo se H1234 è stata rifiutata.
Proprietà della “COERENZA”:
H(12)∩(34): µ1=µ2∩µ3=µ4 (o H1234) implica anche H12: µ1=µ2 e H34: µ3=µ4;
Se H(12)∩(34) (o H1234) non è rifiutata anche H12 e H34 non lo sono.
Proprietà della “CONSONANZA”:
Se H(12)∩(34): µ1=µ2∩µ3=µ4 è rifiutata, allora almeno una delle due ipotesi
che la compongono (H12: µ1=µ2 e H34: µ3=µ4) è rifiutata.
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UNA “SCORCIATOIA” (SHORTCUT) DELLA “CLOSED
TESTING PROCEDURE”
“FREE COMBINATIONS CONDITION” (HOLM, 1979).
Ogni partizione di “ m “ ipotesi in due sotto-insiemi (uno in
cui le ipotesi sono tutte vere e l’altro in cui le ipotesi sono
tutte false) è possibile per almeno qualche punto dello
spazio dei parametri.
Impiego dei cosiddetti “Intersection-Union (UI) tests” che
hanno la proprietà che una qualsiasi “intersezione di una
famiglia di ipotesi” (Roy, 1953: H0=∩ i∈ I H0i, I indice di un
arbitrario set) è rifiutata se e solo se almeno una delle
ipotesi H0i è rifiutata.
La regione di rifiuto per H0 è data dalla unione delle regioni
di rifiuto per le H0i, i ∈ I.
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Se la regione critica (di rifiuto) di ciascuna H0i per i ∈ I è
della forma (T0i > ξ ), la statistica per ogni non minimale
(intersezione) H0 sarà T0 = max i ∈ I T0i e la sua regione
critica sarà T0 > ξ .
I valori di T0 così ottenuti sono definiti “UI statistiche”.
Si dimostra che una “single-step procedure” è coerente e
consonante se e solo se è una UI procedura (Gabriel,
1969).
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Quindi per rifiutare una qualsiasi ipotesi elementare,
diciamo H01, non è necessario saggiare tutte le
“intersezioni ipotesi” che la contengono.
E’ sufficiente saggiare direttamente H01 purché la
procedura di “testing” sia effettuata in una modalità “stepdown” ordinando le H0i [i valori di probabilità
corrispondenti: p(m) ≤ p(m-1) ≤ … ≤ p(1) ] in modo da
assicurare la condizione della coerenza.
Si saggia l’ipotesi corrispondente al minimo valore p(m) al
livello α / m, se questa è rifiutata si saggia l’ipotesi
corrispondente al valore di p(m-1) al livello α / (m - 1) e così
via; altrimenti la procedura si ferma non rifiutando tutte le
ipotesi rimanenti.
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Questa è la “Sequentially Rejective Multiple Test
Procedure” di Holm (1979), che si basa sulla proprietà della
“monotonicità dei valori critici” del test di Bonferroni (p(m) ≤
α/m → p(m) ≤ α/(m –1) → p(m) ≤ α/(m-2)…].
Se dopo aver rifiutato “ j ” ipotesi corrispondenti ai “ j ” più
piccoli valori di p [p(m) ≤ p(m-1) ≤ … ≤ p(m-j+1)] invece di
saggiare la (j+i)-esima ipotesi al livello α / (m - j) la si
saggia al livello α / j* (essendo j* il massimo numero
possibile di vere ipotesi nulle dato che “ j ” ipotesi nulle
sono già state rifiutate), si applica la procedura modificata
proposta da Shaffer (1986).
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PROCEDURE SIMULTANEE
SIMULTANEOUS TESTING PROCEDURES (STP):
Una collezione di test in cui un’ipotesi è rifiutata se T0i > ξ
(i ∈ I) dove ξ è una costante critica comune per tutti i test
(Gabriel, 1969).
(Bonferroni: α* = α / I ; Šidák: α* = 1-(1-α)1 / I)
Tukey: (tutti i confronti a coppie (pairwise): MCA
“studentized range”)
Dunnett: (tutti i confronti con un controllo: MCC)
Scheffé (simultaneous confidence intervals)
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PROCEDURE “STEP-WISE”:
Step-down: dal “più significativo” valore del test (minimo
valore di p) al “meno significativo” (massimo valore di p).
Marcus - Peritz - Gabriel (1976),
Bonferroni-Holm (1979), Šidák-Holm, Simes (1986),
Hommel (1988)
Tukey-Welsch (Ryan, Einot, Gabriel, Welsch)
Dunnett - Tamhane (1991)
Step-up: dal “meno significativo” valore del test (massimo
valore di p) al “più significativo” (minimo valore di p).
Hochberg (1988), Rom (1990), Benjamini – Hochberg
(1995), Dunnett - Tamhane (1992)
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CLASSIFICAZIONE delle MCP IN BASE AI PARAMETRI
SU CUI FARE UNA DIRETTA INFERENZA (HSU, 1996)
ACC: All-Contrasts Comparisons:
k
∑c µ
i
i
i =1
MCA: All-Pairwise Comparisons: µi - µj , per i < j
MCB: Multiple Comparisons with the Best: µi - maxj ≠i µj , per i =1,…,k
MCC: Multiple Comparisons with the Control: µi - µk , per i = 1,…, k - 1
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INTERVALLI DI CONFIDENZA: STP (1)
γi = I’i θ con I’i = (1, -1, 0,..,0m)’; γi = funzione stimabile dei parametri θ.
m
m
H0i: γi = γ0i verso H1: γi = γ1i (1 ≤ i ≤ m): H0 = I i=1H0i verso H1 = U i=1H1i
PrH0 (ti > ξi per qualche i = 1, 2,…,m) = α; ξi = ξ;
UI test rifiuta H0 se max(1 ≤ i ≤ m)ti > ξ; PrH0 (max(1 ≤ i ≤ m)ti > ξ) = α.
Sotto H0, t1, t2,…, tm sono distribuiti secondo una multivariata (mvariata) distribuzione t (Dunnett e Sobel, 1954, Cornish, 1954) con ν
gradi di libertà ed associata matrice di correlazione R={ρij } (1≤i ≠ j≤m).
La regione di confidenza per γ a livello (1-α) è data da:
γ : γi ∈ [
± Tα,m,ν, ( ρij ) s (C)1/2 ] per (1 ≤ i ≤ m)
γ̂ i
Tα,m,ν, ( ρij )è il percentile della multivariata t distribuzione, s è la radice
quadrata della varianza d’errore stimata e C è una costante che
dipende dal contrasto (funzione stimabile) considerato.
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INTERVALLI DI CONFIDENZA: STP (2)
Tα,m,ν, ( ρij ) sostituito da: tα*/2,ν con: α* = α / m (Bonferroni),
α* = 1 - (1-α)1 / m (Dunn-Šidák).
Se ρij = 0:
“Studentized Maximum Modulus Distribution” (Tukey, 1953; Roy e
Bose, 1953) con parametri m ed i gradi di libertà ν: Mα,m,ν.
Per m = k(k-1) / 2 confronti a coppie:
“Studentized Range Distribution” (T-procedure di Tukey, 1953) con
parametri k ed i gradi di libertà ν : Qα,k,ν.
(θi - θj) ∈ [ Yi − Yj ± Qα,k,ν, s (2 / n)1/2 ] per (1 ≤ i ≠ j ≤ m).
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INTERVALLI DI CONFIDENZA (3):
Stepdown Method: Multiple Comparisons with a Control:
No stepwise procedures has a corresponding confidence
set (Lehmann, 1986).
Bofinger (1987), Stefansson et al. (1988), Dunnett e Tamhane (1991):
µ[i] - µk >
µˆ [i] − µˆ k − dk −1σˆ 2n
con d1 (=tα , υ) < d2 < ...<dk-1
quantili della multivariata (2,…, k-2, k-1) t distribuzione.
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MCP - Intervalli di confidenza – Variabili Binomiali (non normali).
Impiego della trasformata arcoseno
arcsin√p ∼ N(arcsin√π, 1/(4n) )
“bootstrap” e “permutation resampling”
(SAS® Proc MULTTEST: single-step / step-down tests)
Fisher’s exact test, Cochran-Armitage linear trend test,
Freeman – Tukey, Peto mortality – prevalence (log-rank).
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Le Guidelines non contemplano la possibilità di confrontare due
trattamenti
mediante
un
“test
globale”
che
considera
congiuntamente “multiple endpoints”.
Metodologia proposta da O’Brien (1984, mediante approccio non
parametrico sui dati trasformati in ranghi e parametrico sui dati
previamente deviati e standardizzati) ed estesa da Pocock et al. (1987
a qualsiasi statistica test con distribuzione asintotica normale).
L’approccio mirava a superare i limiti di un confronto tra due campioni
multivariati (test T2 di Hotelling) la cui ipotesi nulla può essere rifiutata
senza distinguere tra variabili che cambiano a favore o a sfavore di uno
o dell’altro trattamento ed il fatto che la correzione di Bonferroni è
conservativa.
“Theoretically attractive but it is probably of practical use in only a small
minority of trials (Pocock, 1997)”.
Le Guidelines non contemplano l’uso di test globali (analisi della
varianza, test del χ2) per il confronto di più di due trattamenti.
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POTENZA – DIMENSIONE DEL CAMPIONE PER MCP
ICH E9: “If the purpose of the trial is to demonstrate effects on all of the
designated primary variables, then there is no need for adjustment of the type I
error, but the impact on type II error and sample size should be carefully
considered”.
10 indipendenti test, tutti con una potenza individuale dello 0.80, la
COMPLETE POWER = (0.80)10 = 0.10737.
COMPLETE POWER = Pr (rifiutare tutte le H0i che sono false).
MINIMAL POWER = Pr (rifiutare almeno una H0i falsa).
INDIVIDUAL POWER = Pr (rifiutare una particolare H0i che è falsa).
PROPORTIONAL POWER = valore atteso della proporzione di false H0i
che sono rifiutate. Simulando 10,000 studi, si calcola la proporzione di false H0i
che sono rifiutate per ciascun studio e poi si calcola la media di queste
proporzioni per i 10,000 studi.
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POTENZA – DIMENSIONE DEL CAMPIONE PER MCP
Usando l’approccio dei confronti multipli basato sugli intervalli di
confidenza simultanei, la power è definita come la probabilità che gli
intervalli di confidenza comprendano i valori dei veri parametri e siano
sufficientemente “piccoli”.
MULTIPLE COMPARISONS with a CONTROL (MCC):
si deve specificare:
1)- il numero di gruppi (k: il k-esimo è il controllo),
2)- il valore minimo della differenza ω = µi - µk che si vuole dimostrare,
3)- la dimensione iniziale campionaria n (n1, n2,…,nk se non bilanciato),
4)- la potenza (1 - β) richiesta (“Complete Power”),
5)- il livello di significatività (α),
6)- la deviazione standard entro i gruppi (σ).
MULTIPLE COMPARISONS with the BEST (MCB).
ALL-PAIRWISE COMPARISONS (MCA).
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EMEA
The European Agency for the Evaluation of Medicinal Products
Evaluation of Medicines for Human Use
London, 19 September 2002
CPMP/EWP/908/99
COMMITTEE FOR PROPRIETARY MEDICINAL PRODUCTS
(CPMP)
POINTS TO CONSIDER ON MULTIPLICITY ISSUES IN CLINICAL
TRIALS
DISCUSSION IN THE EFFICACY WORKING PARTY
January 2000
TRANSMISSION TO CPMP
JuIy 2001
RELEASE FOR CONSULTATION
JuIy 2001
DEADLINE FOR COMMENTS
DISCUSSION IN THE EFFICACY WORKING PARTY
October 2001
June 2002
TRANSMISSION TO CPMP
September 2002
ADOPTION BY CPMP
September 2002
7 Westferry Circus, Canary Wharf, London, E14 4HB, UK
Tel. (44-20) 74 18 84 00 Fax (44-20) 74 18 8613
E-mail: [email protected] http://www.emea.eu.int
©EMEA 2002 Reproduction and/or distribution of this document is authorised for non commercial purposes only provided the EMEA is acknowledged
POINTS TO CONSIDER ON MULTIPLICITY ISSUES IN CLINICAL
TRIALS
1.
INTRODUCTION
Multip1icity of inferences is present in virtually all clinical trials. The usual concern with multiplicity is that, if
it is not properly handled, unsubstantiated claims for the effectiveness of a drug may be made as a consequence
of an inflated rate of false positive conclusions. For example, if statistical tests are performed on five subgroups,
independently of each other and each at a significance level of 2.5% (one-sided directional hypotheses), the
chance of finding at least one false positive statistically significant test increases to 12%.
This example shows that multiplicity can have a substantial influence on the rate of false positive conclusions
which may affect approval and labelling of an investigational drug whenever there is an opportunity to choose
the most favourable result from two or more analyses. If, however, there is no such choice, then there can be no
influence. Examples of both situations will be discussed later. Control of the study-wise rate of false positive
conclusions at an acceptable level a is an important principle and is often of great value in the assessment of the
results of confirmatory clinical trials.
A number of methods are available for controlling the rate of false positive conclusions, the method of choice
depending on the circumstances. Throughout this document the term ‘control of type I error’ rate will be used as
an abbreviation for the control of the family-wise type I error in the strong sense, i.e., there is control on the
probability to reject at least one true null hypothesis, regardless which subset of null hypotheses happens to be
true. The issue of setting an appropriate type I error level on a submission level when this includes the need for
more than one confirmatory trial is discussed in a separate Points-to-Consider document (CPMP/2330/99 Points
to Consider on Application with 1.) Meta-analyses and 2.) One Pivotal study).
This document does not attempt to address all aspects of multiplicity but mainly considers issues that have been
found to be of importance in recent European applications. These are:
•
Adjustment of multiplicity — when is it necessary and when is it not?
•
How to interpret significance with respect to multiple secondary variables and when can a claim be based
on one of these?
•
When can reliable conclusions be drawn from a subgroup analysis?
•
When is it appropriate for CPMP to restrict licence to a subgroup?
•
How should one interpret the analysis of “responders” in conjunction with the raw variables?
•
How should composite endpoints be handled statistically with respect to regulatory claims?
There are further areas concerning multiplicity in clinical trials which, according the above list of issues, are not
the focus of this document. For example, there is a rapid advance in methodological richness and complexity
regarding interim analyses (with the possibility to stop early either for futility or with the claim of effectiveness)
or stepwise designed studies (with the possibility for adaptive changes for the future steps). However, due to the
importance of the problem and the amount of information specific to this issue it appears appropriate that a
separate document may cover these aspects.
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Interpretations of repeated evaluations of the primary efficacy variable at repeated visits usually do not cause
multiplicity problems, because in the majority of situations either an appropriate summary measure has been
pre-specified or according to the requirements on the duration of treatment endpoint, primary evaluations are
made at pre-specified visits. Therefore potential multiplicity issues concerning the analysis of repeated
measurements are not considered in this document.
2.
ADJUSTMENT FOR MULTIPLICITY - WHEN IS IT NECESSARY AND
WHEN IS IT NOT?
A clinical study that requires no adjustment of the type I error is one that consists of two treatment groups, that
uses a single primary variable, and has a confirmatory statistical strategy that pre-specifies just one single null
hypothesis relating to the primary variable and no interim analysis. Although all other situations require
attention to the potential effects of multiplicity, there are many situations where no multiplicity concern arises,
for example, having predefined the primary variables and all secondary variables are declared supportive.
In the literature, methods to control the overall type I error α are sometimes called multiple-level-α-tests”.
Controlling type I error family-wise often (but not always) means that the accepted and pre-specified amount α
of type I error has to be split, and that the various null hypotheses have to be tested at the resulting fraction of α.
This is usually referred to as ‘adjusting the type I error level’. The algorithms that define how to “spend” α in
this way are of different complexity. Often, for the more complex procedures, clinical interpretation of the
findings can become difficult. For example, for the purpose of estimation and for the appraisal of the precision
of estimates, confidence intervals are of paramount importance but methods for their construction that are
consistent with the tests are not available for many of the more complex multiple-level-α-tests (or more
generally closed tests) aiming at controlling the type I error. When choosing an approach, it is recommended to
consider whether the existing valid statistical procedures allow a satisfactory clinical interpretation.
Because alternative methods to deal correctly with multiplicity are often available which may lead to different
conclusions, pre-definition of the preferred multiple-level-α-tests is necessary. To avoid problems in
interpretation, details of the procedure should be contained in the study protocol or the statistical analysis plan.
If a multiple test situation occurs which was not foreseen, a conservative approach will be necessary e.g.
Bonferroni’s or a related procedure. Inherently there will be a loss of power. Therefore if a multiple test
situation is foreseen pre-specification of the method use to deal with this is recommended
This document discusses situations with relevance for multiple testing in clinical trials and commonly practised
and acknowledged methods for controlling (or adjusting) type I error.
2.1
Multiple primary variables — when no formal adjustment is needed.
The ICH E9 guideline on biostatistical principles in clinical trials recommends that generally clinical trials have
one primary variable. A single primary variable is sufficient, if there is a general agreement that a treatment
induced change in this variable demonstrates a clinical relevant treatment effect on its own. If, however, a single
variable is not sufficient to capture the range of clinically relevant treatment benefits, the use of more than one
primary variable may become necessary. Sometimes a series of related objectives is pursued in the same trial
each with its own primary variable, and in other cases, a number of primary variables are investigated with the
aim of providing convincing evidence of beneficial effects on some, or
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all of them. In these situations planning of the sample size becomes more complex because alternative
hypotheses and limits for the power of the single primary variables have to be defined and balanced against each
other to give the study a solid basis to meet its objectives.
For trials with more than one primary variable the situations described in the following subsections can be
distinguished. The methods described allow clinical interpretation, deal satisfactorily with the issue of
multiplicity but avoid the need for any formal adjustment of type I error rates. Indeed the methods are members
from the set of closed testing procedures that control the family-wise error rate.
2.1.1. Two or more primary variables are needed to describe clinically relevant treatment benefits
Statistical signifìcance is needed for all primary variables. Therefore, no formal adjustment is necessary.
Here, interpretation of the results is most clear-cut because, in order to provide sufficient evidence of the
clinically relevant treatment benefit, each null hypotheses on every primary variable has to be rejected at the
same significance level (e.g. 0.05). For examples of this clinical situation, see CPMP Note for Guidance for the
treatment of Alzheimer’s disease, or CPMP Points to Consider on clinical investigation of medicinal products in
the chronic treatment of patients with chronic obstructive pulmonary disease. In these situations, there is no
intention or opportunity to select the most favourable result and, consequently, the individual type I error levels
are set equal to the overall type I error level α, i.e. no reduction is necessary. This procedure inflates the relevant
type Il error (here: falsely accepting that at least one null hypothesis is true), which in the worst case scenario is
the sum of the type II errors connected with the individual hypotheses. This inflation must be taken into account
for a proper estimation of the sample size for the trial.
2.1.2. Two or more primary variables ranked according to clinical relevance
No formal adjustment is necessary. However, no confirmatory claims can be based on variables that have a
rank lower than or equal to that variable whose null hypothesis was the first that could not be rejected.
Sometimes a series of related objectives is pursued in the same trial, where one objective is of greatest
importance but convincing results in others would clearly add to the value of the treatment. Typical examples
are (i) acute effects in depressive disorders followed by prevention of progression (ii) reduction of mortality in
acute myocardial infarction followed by prevention of other serious events. In such cases the hypotheses may be
tested (and confidence intervals may be provided) according to a hierarchical strategy. The hierarchical order
may be a natural one (e.g. hypotheses are ordered in time or with respect to the seriousness of the considered
variables) or may result from the particular interests of the investigator. Again, no reduction or splitting of α is
necessary. The hierarchical order for testing null hypotheses, however, has to be pre-specified in the study
protocol. The effect of such a procedure is that no confirmatory claims can be based on variables that have a
rank lower than or equal to that variable whose null hypothesis was the first that could not be rejected.
Confidence intervals that are consistent with this hierarchical test procedure can be derived. Evidently, type II
errors are inflated for hypotheses that correspond to variables with lower ranks. Note that a similar procedure
can be used for dealing with secondary variables (see 3.2).
In the literature it is possible to find many methods of dealing with multiple variables that are of value for
situations which may, however, be rarely met in confirmatory clinical trials, and
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which, therefore, are not discussed in this document. Before applying such methods regulatory dialogue is
recommended.
2.2
Analysis sets
Multiple analyses may be performed on the same variable but with varying subsets of patient data. As is pointed
out in the ICH E9 guideline on biostatistical principles for clinical trials, the set of subjects whose data are to be
included in the main analyses should be defined in the statistical section of the study protocol. From these sets
of subjects one (usually the full set) is selected for the primary analysis.
In general, multiple analyses on varying subsets of subjects or with varying measurements for the purpose of
investigating the sensitivity of the conclusions drawn from the primary analysis should not be subjected to
adjustment for type I error. The main purpose of such analyses is to increase confidence in the results obtained
from the primary analysis
2.3
Alternative statistical methods — multiplicity concerns
Different statistical models or statistical techniques (e.g. parametric vs. non-parametric or Wilcoxon test versus
log rank test) are sometimes tried on the same set of data. Sometimes a two step procedure is applied with the
purpose of selecting a particular statistical technique for the main treatment comparison based on the outcome of
the first statistical (pre-) test. Multiplicity concerns would immediately arise, if such procedures offered obvious
opportunities for selecting a favourable analysis strategy based on knowledge of the patients’ assignment to
treatments. There are situations, where selecting the final statistical model based on a formal Blind Review (see
ICH E9) is exempted from such concerns. Opportunities for choice in such procedures are often subtle, when
these procedures use comparative treatment information, and the influence on the overall type I error is difficult
to assess. Finally, the need to change important key features of a study on a post hoc basis may question the
credibility of the study and the robustness of the results with the possible consequence that a further study will
be necessary. Therefore, such procedures cannot be recommended even when it appears that there is no element
of choice.
2.4
Multiplicity in safety variables
When a safety variable is part of the confirmatory strategy of a study and thus has a role in the approval or
labelling claims, it should not be treated differently from the primary efficacy variables, except for the situation
that the observed effects show in the opposite direction and may raise a safety concern (see also 3.3). In the case
of adverse effects p-values are of very limited value as substantial differences (expressed as relative risk or risk
differences) will raise concern, depending on seriousness, severity or outcome, irrespective of the p-value
observed.
In those cases where a large number of statistical test procedures is used to serve as a flagging device to signal a
potential risk caused by the investigational drug it can generally be stated that an adjustment for multiplicity is
counterproductive for considerations of safety. It is clear that in this situation there is no control over the type I
error for a single hypothesis and the importance and plausibility of such results will depend on prior knowledge
of the pharmacology of the drug.
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2.5
Multiplicity concern in studies with more than two treatment arms
As for studies with more than one primary variable, the proper evaluation and interpretation of a study with
more than two treatment arms can become quite complex. This document is not intended to provide an
exhaustive discussion of every issue relating to studies with multiple treatment arms, only rarely have these
more complex designs been applied in confirmatory clinical trials. Therefore, the following discussion is limited
to the more common and simple designs. As a general rule it can be stated that control of the family-wise type I
error in the strong sense (i.e. application of closed test procedures) is a minimal prerequisite for confirmatory
claims. It should be remembered that the usual confidence intervals for the pairwise differences between
treatment groups are — except for a few instances - not consistent with the closed testing procedures, and are
usually too narrow.
2.5.1 The three arm ‘gold standard’ design
For a disease, where a commonly acknowledged reference drug therapy exists, it is often recommended (when
this can be justified on ethical grounds) to demonstrate the efficacy and safety of a new substance in a three arm
study with three treatments: the reference drug, placebo and the investigational drug. Usually the aims of such a
study are manifold: (I) to demonstrate superiority of the investigational drug over placebo (proof of efficacy);
(2) to demonstrate superiority of the reference drug over placebo (proof of assay sensitivity, see ICH E1O,
section 2.5.1.1.1); and (3) to demonstrate that the investigational drug retains most of the efficacy of the
reference drug as compared to placebo (proof of non-inferiority). If all of these are objectives, all three
comparisons must show statistical significance at the required level, and no formal adjustment is necessary. A
failure to show the investigational drug as superior to placebo could then be explained either as the
investigational drug being not effective (when the reference drug showed superiority over placebo), or as lack of
assay sensitivity (when test and reference drug failed to show superiority over placebo).
2.5.2 Proof of efficacy for a fixed combination
For fixed combination medicinal products the corresponding CPMP guideline (CPMP/EWP/240/95) requires
that ‘each substance of a fixed combination must have documented contribution within the combination’. For a
combination with two (mono) components, this requirement has often been interpreted as the need to conduct a
study with the two components as mono therapies and the combination therapy in a 3-arm study. Such a study is
considered successful, if the combination is shown superior to both components. No formal adjustment of the
overall significance level is necessary, because both pairwise comparisons must show statistically significant
superiority.
Multiple-dose factorial designs are employed for the assessment of combination drugs for the purpose (1) to
provide confirmatory evidence that the combination is more effective than either component drug alone (see
ICH E4 Note for Guidance on Dose Response Information to support Drug Registration (CPMP/ICH/378/95)),
and (2) to identify an effective and safe dose combination (or a range of useful dose combinations) for
recommended use in the intended patient population. While (i) usually is achieved using global test strategies,
appropriate closed test procedures have to be applied for the purpose of achieving (2).
2.5.3 Dose-response studies
For therapeutic dose response studies that aim at identifying one or several doses of an investigational drug for
its recommended use in a specific patient population, the control of the family-wise type I error in the strong
sense is mandatory. Due to the large variety of design features, assumptions and aims in such studies (e.g.
assuming or not assuming monotonicity of the dose response with increasing dose; finding the minimally
effective dose under the constrains of the used design; finding a dose that is equivalent (non-inferior) to the
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recommended dose of a reference drug), specific recommendations are beyond the scope of this document.
There are various methods published in the relevant literature on closed test procedures with relevance to
multiple dose studies that can be adapted to the specific aims and that provide the necessary control on the type I
error.
Sometimes a study is not powered sufficiently for the aim to identify and recommend a single effective and safe
dose (or a dose range) but is successful only at demonstrating an overall positive correlation of the clinical effect
with increasing dose. This is already a valuable achievement (sec ICH E4, section 3.1). Estimates and
confidence intervals from pairwise comparisons of single doses are then used in an exploratory manner for the
planning of future studies. In this case, an adjustment of the type I error is not necessary.
3.
HOW TO INTERPRET SIGNIFICANCE WITH RESPECT TO MULTIPLE
SECONDARY VARIABLES AND WHEN CAN A CLAIM BE BASED ON ONE
OF THESE?
Traditionally, in clinical trial protocols there will be a number of secondary variables for efficacy. Up to now
there has been no common consent about the role and the weight of secondary endpoints in clinical trials.
3.1
Variables expressing supportive evidence
No claims are intended; confidence intervals and statistical tests are of exploratory nature.
Secondary endpoints may provide additional clinical characterisation of treatment effects but are, by themselves,
not sufficiently convincing to establish the main evidence in an application for a license or for an additional
labelling claim. Here, the inclusion of secondary variables is intended to yield supportive evidence related to the
primary objective, and no confirmatory conclusions are needed. Confidence intervals and statistical tests are of
exploratory nature and no claims are intended.
3.2
Secondary variables which may become the basis for additional claims
Significant effects in these variables can be considered for an additional claim only after the primary objective
of the clinical trial has been achieved, and if they were part of the confirmatory strategy
More importantly, secondary variables may be related to secondary objectives that become the basis for an
additional claim, once the primary objective has been established (see 2.1.2). A valid procedure, to deal with
this kind of secondary variable is to proceed hierarchically. Once the null hypothesis concerning the primary
objective is rejected (and the primary objective thus established), further confirmatory statistical tests on
secondary variables can be performed using a further hierarchical order for the secondary variables themselves
if there is more than one. In this case, primary and secondary variables differ just in their place in the hierarchy
of hypotheses which, of course, reflects their relative importance in the study. It is of note to mention that
changes in secondary variables that are considered a direct consequence of the respective changes in the primary
variables cannot be part of the labelling claims. For example, symptoms of depression in schizophrenic patients
disappear as patients get into remission from schizophrenia. In this situation, a separate labelling claim on an
anti-depressive action of the treatment cannot be made.
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3.3
Variables indicative of clinical benefit
If not defined as primary variables, clinically very important variables (e.g. mortality) need further study when
significant benefits are observed, but the primary objective has not been achieved.
Variables that have the potential of being indicative of a major clinical benefit or may in a different situation
present an important safety issue (e.g. mortality) may be relegated to secondary variables because there is an a
priori belief that the size of the planned trial is too small (and thus the power too low) to show a benefit. If,
however, the observed beneficial effect is much higher than expected but the study fell short of achieving its
primary objective, this would be a typical situation where information from further studies would be needed
which can be used in support of the observed beneficial effect.
If however, the same variable that may indicate a major clinical benefit exhibits treatment effects in the opposite
direction this would give rise to concerns about the safety. A license may then well be refused, regardless of
whether or not the variable was embedded in a confirmatory scheme.
4.
RELIABLE CONCLUSIONS FROM A SUBGROUP ANALYSIS, AND
RESTRICTION OF THE LICENSE TO A SUBGROUP
Reliable conclusions from subgroup analyses generally require pre-specification and appropriate statistical
analysis strategies. A license may be restricted if unexplained strong heterogeneity is found in important subpopulations, or if heterogeneity of the treatment effect can reasonably be assumed but cannot be sufficiently
evaluated for important sub-populations.
In clinical trials there are many reasons for examining treatment effects in subgroups. In many studies, subgroup
analyses have a supportive or exploratory role after the primary objective has been accomplished, i.e. the
demonstration of a significant overall clinical benefit. A specific claim of a beneficial effect in a particular
subgroup requires pre-specification of the corresponding null hypothesis and an appropriate confirmatory
analysis strategy. It is highly unlikely that claims based on subgroup analyses would be accepted in the absence
of a significant effect for the overall study population. Considerations of power would be expected to be covered
in the protocol, and randomisation would generally be stratified.
The evaluation of uniformity of treatment effects across subgroups is a general regulatory concern. Some factors
are known to cause heterogeneity of treatment effects such as gender, age, region, severity of disease, ethnic
origin, renal impairment, or differences in absorption or metabolism. Analyses of these important subgroups
should be a regular part of the evaluation of a clinical study (when relevant), but should usually be considered
exploratory, unless there is a priori suspicion that one or more of these factors may influence the size of effect.
However, when a strong interaction is found that indicates an adverse effect of the treatment in one of the
subgroups and no convincing explanation for this phenomenon is available or other information confirms the
likelihood of an interaction then patients from the respective sub-population may be excluded from the license
until additional clinical data are available. This may also apply when there are historical reasons for regulators
to believe that a certain sub-population of patients will not benefit from the drug and the results do not strongly
contradict this believe.
Restriction of a license to certain subgroups is also possible, if a large variety of sub-populations are
investigated without proper plans to deal with this situation in the protocol. From the regulatory perspective an
overall positive result (statistically and clinically) in the whole study population may not lead to valid claims for
all sub-populations if there is a
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reason to expect heterogeneity of the treatment effect in the respective sub-populations. If a meaningful
definition of the overall study population is lacking, licensing may be limited to sub. populations which are
adequately represented and in which statistically significant and clinically relevant results were observed.
5.
HOW SHOULD ONE INTERPRET THE ANALYSIS OF “RESPONDERS” IN
CONJUNCTION WITH THE RAW VARIABLES?
If the “responder” analysis is not the primary analysis it may be used after statistical signjficance has been
established on the mean level of the required primary variable(s), to establish the clinical relevance of the
observed djfferences in the proportion of “responders “. When used in this manner, the test of the null
hypothesis of no treatment effect is better carried out on the original primary variable than on the proportion of
responders.
In a number of applications, for example those concerned with Alzheimer’s disease or epileptic disorders, it is
difficult to interpret small but statistically significant improvements in the mean level of the primary variables.
For this reason the term “responder” (and “non-responder”) is used to express the clinical benefit of the
treatment to individual patients. There may be a number of ways to define a “responder”/“nonresponder”. The
definitions should be pre-specified in the protocol and should be clinically convincing. In clinical guidelines, it
is stated that the “responder” analysis should be used in establishing the clinical relevance of the observed effect
as an aid to assess efficacy and clinical safety. It should be noted that there is some loss of information (and
hence loss of statistical power) connected with breaking down the information contained in the original
variables unto “responder” and “non-responder”.
In some situations, the “responder” criterion may be the primary endpoint (e.g. CPMP guideline on clinical
investigation of medicinal products in the treatment of Parkinson’s disease). In this case it should be used to
provide the main test of the null hypothesis. However, the situation that is primarily addressed here is when the
“responder” analysis is used to show a judgement on clinical relevance, once a statistically significant treatment
effect on the mean level of the primary variable(s) loss been established (e.g. CPMP Note for Guidance on
clinical investigation of drugs used in weight control, or on the treatment of Alzheimer’s disease). In this case,
the results of the “responder” analysis need not be statistically significant but the difference in the proportions of
responders should support a statement that the investigated treatment induces clinically relevant effects.
It should be noted that a “responder” analysis cannot rescue otherwise disappointing results on the primary
variables.
6.
HOW SHOULD COMPOSITE VARIABLES BE HANDLED STATISTICALLY
WITH RESPECT TO REGULATORY CLAIMS?
Usually, the composite variable is primary. All components should be analysed separately. If claims are based
on subgroups of components, this needs to be pre-specified and embedded in a valid confirmatory analysis
strategy. Treatment should beneficially affect all components, or at least should the clinically more important
components not be affected negatively. Any effect of the treatment in one of the components that Is to be
reflected in the indication should be clearly supported by the data.
There are two types of composite variables. The first type, namely the rating scale, arises as a combination of
multiple clinical measurements. With this type there is a longstanding
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experience of its use in certain indications (e.g. psychiatric or neurological disorders). This type of composite
variable is not discussed further in this guideline.
The other type of a composite variable arises in the context of survival analysis. Several events are combined to
define a composite outcome. A patient is said to have the clinical outcome if s/he suffers from one or more
events in a pre-specified list of components (e.g. death, myocardial infarction or disabling stroke). The time to
outcome is measured as the time from randomisation of the patient to the first occurrence of any of the events in
the list. Usually, the components represent relatively rare events, and to study each component separately would
require unmanageably large sample sizes. Composite variables therefore present a means to increase the
percentage of patients that reach the clinical outcome, and hence the power of the study.
6.1
The composite variable as the primary endpoint.
When a composite variable is used to show efficacy it will usually be the primary endpoint. Therefore, it must
meet the requirements for a single primary endpoint, namely that it is capable of providing the key evidence of
efficacy that is needed for a license. It is recommended to analyse in addition the single components and
clinically relevant groups of components separately, to provide supportive information. There is, however, no
need for an adjustment for multiplicity provided significance of the primary endpoint is achieved. If claims are
to be based on subgroups of components, this needs to be pre-specified and embedded in a valid confirmatory
analysis strategy.
6.2
Treatment should be expected to affect all components in a similar way.
When defining a composite variable it is recommended to include only components for which it can be assumed
that treatment will influence them similarly. The assumption of similarly directed treatment effects on all
components should be based on past experience with studies of similar type. Adding a component that
foreseeably is insensitive to treatment effects will lead to an increase in variability, even if it does not affect
unbiasedness of the estimation of the treatment difference. A direct consequence would be a decrease in
sensitivity for demonstrating superiority between different treatment arms. An increased variance is also a
undesirable property in non-inferiority or equivalence studies. Non-inferiority studies will be hard to interpret if
negative effects on some components are observed. For studies aiming to show superiority the more general
component is preferred as primary endpoint as this is the most conservative analysis. For noninferiority/equivalence studies the more specific component (e.g. disease related mortality) are preferred as
primary endpoint for the same reason.
6.3
The clinically more important components should at least not be affected negatively
If time to hospitalisation is an endpoint in a clinical study it is not generally appropriate to handle patients as
censored who die before they reach the hospital. It is better practice to study a composite endpoint that includes
all more important clinical events as components, including death in this example. One concern with composite
outcome measures from a regulatory point of view is, however, the possibility that some of the treatments under
study may have an adverse effect on one or more of the components, and that this adverse effect is masked by
the composite outcome, e.g. by a large beneficial effect on some of the remaining components. This concern is
particularly relevant, if the components relate to different degrees of disease severity or clinical importance. For
example, if all cause mortality is a component, a separate analysis of all cause mortality should be provided to
ensure that there is no adverse effect on this endpoint. Since there is no general agreement how much less then
statistical significance in the wrong direction will generate suspicion of an adverse effect, a
CPMP/EWP/908/99
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©EMEA 2002
way to create confidence in support of ‘no adverse effect’, once the data is observed, is to address this issue at
the planning stage. For example, the study plan could address the size of the risk of an adverse effect on the
more serious components that can be excluded (assuming no treatment difference under the null hypothesis)
with a sufficiently high probability, given the planned sample size, and the study report should contain the
respective comparative estimates and confidence intervals.
6.4
Any effect of the treatment on one of the components that is to be reflected in the indication should
be clearly supported by the data.
An important issue for consideration is the claim that can legitimately be made based on a successful primary
analysis of a composite endpoint. Difficulties arise if the claims do not properly reflect the fact that a composite
endpoint was used, e.g. if a claim is made that explicitly involves a component with the low occurrence. For
example, if the composite outcome is ‘death or liver transplantation’ and there are only a few deaths, a claim ‘to
reduce mortality and the necessity for liver transplantation’ would not be satisfactory, because in this context the
effect on mortality will have a weak basis. This does not mean that one should drop the component ‘death’ from
the composite outcome, because the outcome ‘liver transplantation’ would be incomplete without
simultaneously considering all disease related outcomes that are at least as serious as ‘liver transplantation’.
However, it does mean that different wording should be adopted for the indication, avoiding the implication of
an effect on mortality.
7.
CONCLUSION
In clinical studies it is often necessary to answer more than one question about the efficacy (or safety) of one or
more treatments in a specific disease, because the success of a drug development program may depend on a
positive answer to more than a single question. It is well known that the chance of a spurious positive chance
finding increases with the number of questions posed, if no actions are taken to protect against the inflation of
false positive findings from multiple statistical tests. In this context, concern is focused on the opportunity to
choose favourable results from multiple analyses. It is therefore necessary that the statistical procedures planned
to deal with, or to avoid, multiplicity are fully detailed in the study protocol or in the statistical analysis plan to
show an assessment of their suitability and appropriateness.
Various different methods have been developed to control the rate of false positive findings. Not all of these
methods, however, are equally successful at providing clinically interpretable results and this aspect of the
procedure should always be considered. Since estimation of treatment effects is usually an important issue, the
availability of confidence intervals connected with a particular procedure may be a criterion for its selection.
Additional claims on statistically significant and clinically relevant findings based on secondary variables or on
subgroups are possible only after the primary objective of the clinical trial has been achieved, and if the
respective questions were pre-specified, and were part of an appropriately planned statistical analysis strategy.
CPMP/EWP/908/99
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©EMEA 2002
INTERNATIONAL CONFERENCE ON HARMONISATION OF TECHNICAL
REQUIREMENTS FOR REGISTRATION OF PHARMACEUTICALS FOR HUMAN
USE
ICH H ARMONISED TRIPARTITE GUIDELINE
S TATISTICAL PRINCIPLES FOR CLINICAL TRIALS
Recommended for Adoption
at Step 4 of the ICH Process
on 5 February 1998
by the ICH Steering Committee
This Guideline has been developed by the appropriate ICH Expert Working Group
and has been subject to consultation by the regulatory parties, in accordance with the
ICH Process. At Step 4 of the Process the final draft is recommended for adoption to
the regulatory bodies of the European Union, Japan and USA.
ICH Harmonised Tripartite Guideline
Having reached Step 4 of the ICH Process at the ICH Steering Committee meeting
on 5 February 1998, this guideline is recommended for
adoption to the three regulatory parties to ICH
TABLE OF CONTENTS
I.
INTRODUCTION ................................ ................................ .............................. 1
1.1 Background and Purpose.............................................................................. 1
1.2 Scope and Direction ...................................................................................... 2
II.
CONSIDERATIONS FOR OVERALL CLINICAL DEVELOPMENT
........ 3
2.1 Trial Context................................................................................................. 3
2.1.1
Development Plan ...................................................................... 3
2.1.2
Confirmatory Trial ..................................................................... 4
2.1.3
Exploratory Trial ........................................................................ 4
2.2 Scope of Trials ............................................................................................... 4
2.2.1
Population................................................................................... 4
2.2.2
Primary and Secondary Variables ............................................. 5
2.2.3
Composite Variables ................................................................... 6
2.2.4
Global Assessment Variables ..................................................... 6
2.2.5
Multiple Primary Variables ....................................................... 7
2.2.6
Surrogate Variables .................................................................... 7
2.2.7
Categorised Variables................................................................. 7
2.3 Design Techniques to Avoid Bias ................................................................. 8
III.
2.3.1
Blinding....................................................................................... 8
2.3.2
Randomisation ............................................................................ 9
TRIAL DESIGN CONSIDERATIONS ................................ .......................... 11
3.1 Design Configuration.................................................................................. 11
3.1.1
Parallel Group Design .............................................................. 11
3.1.2
Crossover Design ...................................................................... 11
3.1.3
Factorial Designs ...................................................................... 12
3.2 Multicentre Trials ....................................................................................... 12
3.3 Type of Comparison .................................................................................... 14
3.3.1
Trials to Show Superiority ....................................................... 14
3.3.2
Trials to Show Equivalence or Non-inferiority ........................ 15
3.3.3
Trials to Show Dose-response Relationship ............................. 16
i
Statistical Principles for Clinical Trials
3.4 Group Sequential Designs.......................................................................... 16
3.5 Sample Size ................................................................................................ 17
3.6 Data Capture and Processing .................................................................... 18
IV.
TRIAL CONDUCT CONSIDERATIONS ................................ ..................... 18
4.1 Trial Monitoring and Interim Analysis ..................................................... 18
4.2 Changes in Inclusion and Exclusion Criteria ............................................ 19
4.3 Accrual Rates.............................................................................................. 19
4.4 Sample Size Adjustment ............................................................................ 19
4.5 Interim Analysis and Early Stopping ........................................................ 20
4.6 Role of Independent Data Monitoring Committee (IDMC) ....................... 21
V.
DATA ANALYSIS CONSIDERATI ONS ................................ ....................... 21
5.1 Prespecification of the Analysis ................................................................. 21
5.2 Analysis Sets .............................................................................................. 22
5.2.1
Full Analysis Set ...................................................................... 22
5.2.2
Per Protocol Set........................................................................ 24
5.2.3
Roles of the Different Analysis Sets ........................................ 24
5.3 Missing Values and Outliers ...................................................................... 25
5.4 Data Transformation.................................................................................. 25
5.5 Estimation, Confidence Intervals and Hypothesis Testing ....................... 26
5.6 Adjustment of Significance and Confidence Levels ................................... 26
5.7 Subgroups, Interactions and Covariates ................................................... 27
5.8 Integrity of Data and Computer Software Validity ................................... 27
VI.
EVALUATION OF SAFETY AND TOLE RABILITY ................................ .. 28
6.1 Scope of Evaluation .................................................................................... 28
6.2 Choice of Variables and Data Collection .................................................... 28
6.3 Set of Subjects to be Evaluated and Presentation of Data ....................... 28
6.4 Statistical Evaluation................................................................................. 29
6.5 Integrated Summary .................................................................................. 30
VII.
REPORTING ................................ ................................ ................................ .. 30
7.1 Evaluation and Reporting .......................................................................... 30
7.2 Summarising the Clinical Database .......................................................... 31
7.2.1
Efficacy Data ............................................................................ 32
7.2.2
Safety Data............................................................................... 32
GLOSSARY
................................ ................................ ................................ .. 33
ii
I.
INTRODUCTION
1.1 Background and Purpose
The efficacy and safety of medicinal products should be demonstrated by clinical trials
which follow the guidance in 'Good Clinical Practice: Consolidated Guideline' (ICH E6)
adopted by the ICH, 1 May 1996. The role of statistics in clinical trial design and
analysis is acknowledged as essential in that ICH guideline. The proliferation of
statistical research in the area of clinical trials coupled with the critical role of clinical
research in the drug approval process and health care in general necessitate a
succinct document on statistical issues related to clinical trials. This guidance is
written primarily to attempt to harmonise the principles of statistical methodology
applied to clinical trials for marketing applications submitted in Europe, Japan and
the United States.
As a starting point, this guideline utilised the CPMP (Committee for Proprietary
Medicinal Products) Note for Guidance entitled 'Biostatistical Methodology in Clinical
Trials in Applications for Marketing Authorisations for Medicinal Products'
(December, 1994). It was also influenced by 'Guidelines on the Statistical Analysis of
Clinical Studies' (March, 1992) from the Japanese Ministry of Health and Welfare
and the U.S. Food and Drug Administration document entitled 'Guideline for the
Format and Content of the Clinical and Statistical Sections of a New Drug
Application' (July, 1988). Some topics related to statistical principles and methodology
are also embedded within other ICH guidelines, particularly those listed below. The
specific guidance that contains related text will be identified in various sections of this
document.
E1A:
The Extent of Population Exposure to Assess Clinical Safety
E2A:
Clinical Safety Data Management: Definitions and Standards for
Expedited Reporting
E2B:
Clinical Safety Data Management: Data Elements for Transmission of
Individual Case Safety Reports
E2C:
Clinical Safety Data Management: Periodic Safety Update Reports for
Marketed Drugs
E3:
Structure and Content of Clinical Study Reports
E4:
Dose-Response Information to Support Drug Registration
E5:
Ethnic Factors in the Acceptability of Foreign Clinical Data
E6:
Good Clinical Practice: Consolidated Guideline
E7:
Studies in Support of Special Populations: Geriatrics
E8:
General Considerations for Clinical Trials
E10:
Choice of Control Group in Clinical Trials
M1:
Standardisation of Medical Terminology for Regulatory Purposes
M3:
Non-Clinical Safety Studies for the Conduct of Human Clinical Trials
for Pharmaceuticals.
1
This guidance is intended to give direction to sponsors in the design, conduct,
analysis, and evaluation of clinical trials of an investigational product in the context
of its overall clinical development. The document will also assist scientific experts
charged with preparing application summaries or assessing evidence of efficacy and
safety, principally from clinical trials in later phases of development.
1.2 Scope and Direction
The focus of this guidance is on statistical principles. It does not address the use of
specific statistical procedures or methods. Specific procedural steps to ensure that
principles are implemented properly are the responsibility of the sponsor. Integration
of data across clinical trials is discussed, but is not a primary focus of this guidance.
Selected principles and procedures related to data management or clinical trial
monitoring activities are covered in other ICH guidelines and are not addressed here.
This guidance should be of interest to individuals from a broad range of scientific
disciplines. However, it is assumed that the actual responsibility for all statistical
work associated with clinical trials will lie with an appropriately qualified and
experienced statistician, as indicated in ICH E6. The role and responsibility of the
trial statistician (see Glossary), in collaboration with other clinical trial professionals,
is to ensure that statistical principles are applied appropriately in clinical trials
supporting drug development. Thus, the trial statistician should have a combination
of education/training and experience sufficient to implement the principles articulated
in this guidance.
For each clinical trial contributing to a marketing application, all important details of
its design and conduct and the principal features of its proposed statistical analysis
should be clearly specified in a protocol written before the trial begins. The extent to
which the procedures in the protocol are followed and the primary analysis is planned
a priori will contribute to the degree of confidence in the final results and conclusions
of the trial. The protocol and subsequent amendments should be approved by the
responsible personnel, including the trial statistician. The trial statistician should
ensure that the protocol and any amendments cover all relevant statistical issues
clearly and accurately, using technical terminology as appropriate.
The principles outlined in this guidance are primarily relevant to clinical trials
conducted in the later phases of development, many of which are confirmatory trials
of efficacy. In addition to efficacy, confirmatory trials may have as their primary
variable a safety variable (e.g. an adverse event, a clinical laboratory variable or an
electrocardiographic measure), a pharmacodynamic or a pharmacokinetic variable (as
in a confirmatory bioequivalence trial). Furthermore, some confirmatory findings may
be derived from data integrated across trials, and selected principles in this guidance
are applicable in this situation. Finally, although the early phases of drug
development consist mainly of clinical trials that are exploratory in nature, statistical
principles are also relevant to these clinical trials. Hence, the substance of this
document should be applied as far as possible to all phases of clinical development.
Many of the principles delineated in this guidance deal with minimising bias (see
Glossary) and maximising precision. As used in this guidance, the term 'bias'
describes the systematic tendency of any factors associated with the design, conduct,
analysis and interpretation of the results of clinical trials to make the estimate of a
treatment effect (see Glossary) deviate from its true value. It is important to identify
potential sources of bias as completely as possible so that attempts to limit such bias
may be made. The presence of bias may seriously compromise the ability to draw
valid conclusions from clinical trials.
2
Some sources of bias arise from the design of the trial, for example an assignment of
treatments such that subjects at lower risk are systematically assigned to one
treatment. Other sources of bias arise during the conduct and analysis of a clinical
trial. For example, protocol violations and exclusion of subjects from analysis based
upon knowledge of subject outcomes are possible sources of bias that may affect the
accurate assessment of the treatment effect. Because bias can occur in subtle or
unknown ways and its effect is not measurable directly, it is important to evaluate
the robustness of the results and primary conclusions of the trial. Robustness is a
concept that refers to the sensitivity of the overall conclusions to various limitations of
the data, assumptions, and analytic approaches to data analysis. Robustness implies
that the treatment effect and primary conclusions of the trial are not substantially
affected when analyses are carried out based on alternative assumptions or analytic
approaches. The interpretation of statistical measures of uncertainty of the treatment
effect and treatment comparisons should involve consideration of the potential
contribution of bias to the p-value, confidence interval, or inference.
Because the predominant approaches to the design and analysis of clinical trials have
been based on frequentist statistical methods, the guidance largely refers to the use of
frequentist methods (see Glossary) when discussing hypothesis testing and/or
confidence intervals. This should not be taken to imply that other approaches are not
appropriate: the use of Bayesian (see Glossary) and other approaches may be
considered when the reasons for their use are clear and when the resulting
conclusions are sufficiently robust.
II. CONSIDERATIONS FOR OVERALL CLINICAL DEVELOPMENT
2.1 Trial Context
2.1.1 Development Plan
The broad aim of the process of clinical development of a new drug is to find out
whether there is a dose range and schedule at which the drug can be shown to be
simultaneously safe and effective, to the extent that the risk-benefit relationship is
acceptable. The particular subjects who may benefit from the drug, and the specific
indications for its use, also need to be defined.
Satisfying these broad aims usually requires an ordered programme of clinical trials,
each with its own specific objectives (see ICH E8). This should be specified in a clinical
plan, or a series of plans, with appropriate decision points and flexibility to allow
modification as knowledge accumulates. A marketing application should clearly
describe the main content of such plans, and the contribution made by each trial.
Interpretation and assessment of the evidence from the total programme of trials
involves synthesis of the evidence from the individual trials (see Section 7.2). This is
facilitated by ensuring that common standards are adopted for a number of features
of the trials such as dictionaries of medical terms, definition and timing of the main
measurements, handling of protocol deviations and so on. A statistical summary,
overview or meta-analysis (see Glossary) may be informative when medical questions
are addressed in more than one trial. Where possible this should be envisaged in the
plan so that the relevant trials are clearly identified and any necessary common
features of their designs are specified in advance. Other major statistical issues (if
any) that are expected to affect a number of trials in a common plan should be
addressed in that plan.
2.1.2 Confirmatory Trial
3
A confirmatory trial is an adequately controlled trial in which the hypotheses are
stated in advance and evaluated. As a rule, confirmatory trials are necessary to
provide firm evidence of efficacy or safety. In such trials the key hypothesis of interest
follows directly from the trial’s primary objective, is always pre-defined, and is the
hypothesis that is subsequently tested when the trial is complete. In a confirmatory
trial it is equally important to estimate with due precision the size of the effects
attributable to the treatment of interest and to relate these effects to their clinical
significance.
Confirmatory trials are intended to provide firm evidence in support of claims and
hence adherence to protocols and standard operating procedures is particularly
important; unavoidable changes should be explained and documented, and their effect
examined. A justification of the design of each such trial, and of other important
statistical aspects such as the principal features of the planned analysis, should be set
out in the protocol. Each trial should address only a limited number of questions.
Firm evidence in support of claims requires that the results of the confirmatory trials
demonstrate that the investigational product under test has clinical benefits. The
confirmatory trials should therefore be sufficient to answer each key clinical question
relevant to the efficacy or safety claim clearly and definitively. In addition, it is
important that the basis for generalisation (see Glossary) to the intended patient
population is understood and explained; this may also influence the number and type
(e.g. specialist or general practitioner) of centres and/or trials needed. The results of
the confirmatory trial(s) should be robust. In some circumstances the weight of
evidence from a single confirmatory trial may be sufficient.
2.1.3 Exploratory Trial
The rationale and design of confirmatory trials nearly always rests on earlier clinical
work carried out in a series of exploratory studies. Like all clinical trials, these
exploratory studies should have clear and precise objectives. However, in contrast to
confirmatory trials, their objectives may not always lead to simple tests of pre-defined
hypotheses. In addition, exploratory trials may sometimes require a more flexible
approach to design so that changes can be made in response to accumulating results.
Their analysis may entail data exploration; tests of hypothesis may be carried out,
but the choice of hypothesis may be data dependent. Such trials cannot be the basis of
the formal proof of efficacy, although they may contribute to the total body of relevant
evidence.
Any individual trial may have both confirmatory and exploratory aspects. For
example, in most confirmatory trials the data are also subjected to exploratory
analyses which serve as a basis for explaining or supporting their findings and for
suggesting further hypotheses for later research. The protocol should make a clear
distinction between the aspects of a trial which will be used for confirmatory proof
and the aspects which will provide data for exploratory analysis.
2.2 Scope of Trials
2.2.1 Population
In the earlier phases of drug development the choice of subjects for a clinical trial may
be heavily influenced by the wish to maximise the chance of observing specific clinical
effects of interest, and hence they may come from a very narrow subgroup of the total
patient population for which the drug may eventually be indicated. However by the
time the confirmatory trials are undertaken, the subjects in the trials should more
closely mirror the target population. Hence, in these trials it is generally helpful to
relax the inclusion and exclusion criteria as much as possible within the target
4
population, while maintaining sufficient homogeneity to permit precise estimation of
treatment effects. No individual clinical trial can be expected to be totally
representative of future users, because of the possible influences of geographical
location, the time when it is conducted, the medical practices of the particular
investigator(s) and clinics, and so on. However the influence of such factors should be
reduced wherever possible, and subsequently discussed during the interpretation of
the trial results.
2.2.2 Primary and S econdary Variables
The primary variable (‘target’ variable, primary endpoint) should be the variable
capable of providing the most clinically relevant and convincing evidence directly
related to the primary objective of the trial. There should generally be only one
primary variable. This will usually be an efficacy variable, because the primary
objective of most confirmatory trials is to provide strong scientific evidence regarding
efficacy. Safety/tolerability may sometimes be the primary variable, and will always
be an important consideration. Measurements relating to quality of life and health
economics are further potential primary variables. The selection of the primary
variable should reflect the accepted norms and standards in the relevant field of
research. The use of a reliable and validated variable with which experience has been
gained either in earlier studies or in published literature is recommended. There
should be sufficient evidence that the primary variable can provide a valid and
reliable measure of some clinically relevant and important treatment benefit in the
patient population described by the inclusion and exclusion criteria. The primary
variable should generally be the one used when estimating the sample size (see
section 3.5).
In many cases, the approach to assessing subject outcome may not be straightforward
and should be carefully defined. For example, it is inadequate to specify mortality as a
primary variable without further clarification; mortality may be assessed by
comparing proportions alive at fixed points in time, or by comparing overall
distributions of survival times over a specified interval. Another common example is a
recurring event; the measure of treatment effect may again be a simple dichotomous
variable (any occurrence during a specified interval), time to first occurrence, rate of
occurrence (events per time units of observation), etc. The assessment of functional
status over time in studying treatment for chronic disease presents other challenges
in selection of the primary variable. There are many possible approaches, such as
comparisons of the assessments done at the beginning and end of the interval of
observation, comparisons of slopes calculated from all assessments throughout the
interval, comparisons of the proportions of subjects exceeding or declining beyond a
specified threshold, or comparisons based on methods for repeated measures data. To
avoid multiplicity concerns arising from post hoc definitions, it is critical to specify in
the protocol the precise definition of the primary variable as it will be used in the
statistical analysis. In addition, the clinical relevance of the specific primary variable
selected and the validity of the associated measurement procedures will generally
need to be addressed and justified in the protocol.
The primary variable should be specified in the protocol, along with the rationale for
its selection. Redefinition of the primary variable after unblinding will almost always
be unacceptable, since the biases this introduces are difficult to assess. When the
clinical effect defined by the primary objective is to be measured in more than one
way, the protocol should identify one of the measurements as the primary variable on
the basis of clinical relevance, importance, objectivity, and/or other relevant
characteristics, whenever such selection is feasible.
5
Secondary variables are either supportive measurements related to the primary
objective or measurements of effects related to the secondary objectives. Their predefinition in the protocol is also important, as well as an explanation of their relative
importance and roles in interpretation of trial results. The number of secondary
variables should be limited and should be related to the limited number of questions
to be answered in the trial.
2.2.3 Composite Variables
If a single primary variable cannot be selected from multiple measurements
associated with the primary objective, another useful strategy is to integrate or
combine the multiple measurements into a single or 'composite' variable, using a predefined algorithm. Indeed, the primary variable sometimes arises as a combination of
multiple clinical measurements (e.g. the rating scales used in arthritis, psychiatric
disorders and elsewhere). This approach addresses the multiplicity problem without
requiring adjustment to the type I error. The method of combining the multiple
measurements should be specified in the protocol, and an interpretation of the
resulting scale should be provided in terms of the size of a clinically relevant benefit.
When a composite variable is used as a primary variable, the components of this
variable may sometimes be analysed separately, where clinically meaningful and
validated. When a rating scale is used as a primary variable, it is especially important
to address such factors as content validity (see Glossary), inter- and intra-rater
reliability (see Glossary) and responsiveness for detecting changes in the severity of
disease.
2.2.4 Global Assessment Variables
In some cases, 'global assessment' variables (see Glossary) are developed to measure
the overall safety, overall efficacy, and/or overall usefulness of a treatment. This type
of variable integrates objective variables and the investigator’s overall impression
about the state or change in the state of the subject, and is usually a scale of ordered
categorical ratings. Global assessments of overall efficacy are well established in some
therapeutic areas, such as neurology and psychiatry.
Global assessment variables generally have a subjective component. When a global
assessment variable is used as a primary or secondary variable, fuller details of the
scale should be included in the protocol with respect to:
1)
the relevance of the scale to the primary objective of the trial;
2)
the basis for the validity and reliability of the scale;
3)
how to utilise the data collected on an individual subject to assign him/her to a
unique category of the scale;
4)
how to assign subjects with missing data to a unique category of the scale, or
otherwise evaluate them.
If objective variables are considered by the investigator when making a global
assessment, then those objective variables should be considered as additional
primary, or at least important secondary, variables.
Global assessment of usefulness integrates components of both benefit and risk and
reflects the decision making process of the treating physician, who must weigh benefit
and risk in making product use decisions. A problem with global usefulness variables
is that their use could in some cases lead to the result of two products being declared
equivalent despite having very different profiles of beneficial and adverse effects. For
example, judging the global usefulness of a treatment as equivalent or superior to an
6
alternative may mask the fact that it has little or no efficacy but fewer adverse
effects. Therefore it is not advisable to use a global usefulness variable as a primary
variable. If global usefulness is specified as primary, it is important to consider
specific efficacy and safety outcomes separately as additional primary variables.
2.2.5 Multiple Primary Variables
It may sometimes be desirable to use more than one primary variable, each of which
(or a subset of which) could be sufficient to cover the range of effects of the therapies.
The planned manner of interpretation of this type of evidence should be carefully
spelled out. It should be clear whether an impact on any of the variables, some
minimum number of them, or all of them, would be considered necessary to achieve
the trial objectives. The primary hypothesis or hypotheses and parameters of interest
(e.g. mean, percentage, distribution) should be clearly stated with respect to the
primary variables identified, and the approach to statistical inference described. The
effect on the type I error should be explained because of the potential for multiplicity
problems (see Section 5.6); the method of controlling type I error should be given in
the protocol. The extent of intercorrelation among the proposed primary variables
may be considered in evaluating the impact on type I error. If the purpose of the trial
is to demonstrate effects on all of the designated primary variables, then there is no
need for adjustment of the type I error, but the impact on type II error and sample
size should be carefully considered.
2.2.6 Surrogate Variables
When direct assessment of the clinical benefit to the subject through observing actual
clinical efficacy is not practical, indirect criteria (surrogate variables - see Glossary)
may be considered. Commonly accepted surrogate variables are used in a number of
indications where they are believed to be reliable predictors of clinical benefit. There
are two principal concerns with the introduction of any proposed surrogate variable.
First, it may not be a true predictor of the clinical outcome of interest. For example it
may measure treatment activity associated with one specific pharmacological
mechanism, but may not provide full information on the range of actions and ultimate
effects of the treatment, whether positive or negative. There have been many
instances where treatments showing a highly positive effect on a proposed surrogate
have ultimately been shown to be detrimental to the subjects' clinical outcome;
conversely, there are cases of treatments conferring clinical benefit without
measurable impact on proposed surrogates. Secondly, proposed surrogate variables
may not yield a quantitative measure of clinical benefit that can be weighed directly
against adverse effects. Statistical criteria for validating surrogate variables have
been proposed but the experience with their use is relatively limited. In practice, the
strength of the evidence for surrogacy depends upon (i) the biological plausibility of
the relationship, (ii) the demonstration in epidemiological studies of the prognostic
value of the surrogate for the clinical outcome and (iii) evidence from clinical trials
that treatment effects on the surrogate correspond to effects on the clinical outcome.
Relationships between clinical and surrogate variables for one product do not
necessarily apply to a product with a different mode of action for treating the same
disease.
2.2.7 Categorised Variables
Dichotomisation or other categorisation of continuous or ordinal variables may
sometimes be desirable. Criteria of 'success' and 'response' are common examples of
dichotomies which require precise specification in terms of, for example, a minimum
percentage improvement (relative to baseline) in a continuous variable, or a ranking
categorised as at or above some threshold level (e.g., 'good') on an ordinal rating scale.
7
The reduction of diastolic blood pressure below 90mmHg is a common
dichotomisation. Categorisations are most useful when they have clear clinical
relevance. The criteria for categorisation should be pre-defined and specified in the
protocol, as knowledge of trial results could easily bias the choice of such criteria.
Because categorisation normally implies a loss of information, a consequence will be a
loss of power in the analysis; this should be accounted for in the sample size
calculation.
2.3 Design Techniques to Avoid Bias
The most important design techniques for avoiding bias in clinical trials are blinding
and randomisation, and these should be normal features of most controlled clinical
trials intended to be included in a marketing application. Most such trials follow a
double-blind approach in which treatments are pre-packed in accordance with a
suitable randomisation schedule, and supplied to the trial centre(s) labelled only with
the subject number and the treatment period so that no one involved in the conduct of
the trial is aware of the specific treatment allocated to any particular subject, not
even as a code letter. This approach will be assumed in Section 2.3.1 and most of
Section 2.3.2, exceptions being considered at the end.
Bias can also be reduced at the design stage by specifying procedures in the protocol
aimed at minimising any anticipated irregularities in trial conduct that might impair
a satisfactory analysis, including various types of protocol violations, withdrawals and
missing values. The protocol should consider ways both to reduce the frequency of
such problems, and also to handle the problems that do occur in the analysis of data.
2.3.1 Blinding
Blinding or masking is intended to limit the occurrence of conscious and unconscious
bias in the conduct and interpretation of a clinical trial arising from the influence
which the knowledge of treatment may have on the recruitment and allocation of
subjects, their subsequent care, the attitudes of subjects to the treatments, the
assessment of end-points, the handling of withdrawals, the exclusion of data from
analysis, and so on. The essential aim is to prevent identification of the treatments
until all such opportunities for bias have passed.
A double-blind trial is one in which neither the subject nor any of the investigator or
sponsor staff who are involved in the treatment or clinical evaluation of the subjects
are aware of the treatment received. This includes anyone determining subject
eligibility, evaluating endpoints, or assessing compliance with the protocol. This level
of blinding is maintained throughout the conduct of the trial, and only when the data
are cleaned to an acceptable level of quality will appropriate personnel be unblinded.
If any of the sponsor staff who are not involved in the treatment or clinical evaluation
of the subjects are required to be unblinded to the treatment code (e.g. bioanalytical
scientists, auditors, those involved in serious adverse event reporting), the sponsor
should have adequate standard operating procedures to guard against inappropriate
dissemination of treatment codes. In a single-blind trial the investigator and/or his
staff are aware of the treatment but the subject is not, or vice versa. In an open-label
trial the identity of treatment is known to all. The double-blind trial is the optimal
approach. This requires that the treatments to be applied during the trial cannot be
distinguished (appearance, taste, etc.) either before or during administration, and
that the blind is maintained appropriately during the whole trial.
Difficulties in achieving the double-blind ideal can arise: the treatments may be of a
completely different nature, for example, surgery and drug therapy; two drugs may
have different formulations and, although they could be made indistinguishable by
the use of capsules, changing the formulation might also change the pharmacokinetic
8
and/or pharmacodynamic properties and hence require that bioequivalence of the
formulations be established; the daily pattern of administration of two treatments
may differ. One way of achieving double-blind conditions under these circumstances is
to use a 'double-dummy' (see Glossary) technique. This technique may sometimes
force an administration scheme that is sufficiently unusual to influence adversely the
motivation and compliance of the subjects. Ethical difficulties may also interfere with
its use when, for example, it entails dummy operative procedures. Nevertheless,
extensive efforts should be made to overcome these difficulties.
The double-blind nature of some clinical trials may be partially compromised by
apparent treatment induced effects. In such cases, blinding may be improved by
blinding investigators and relevant sponsor staff to certain test results (e.g. selected
clinical laboratory measures). Similar approaches (see below) to minimising bias in
open-label trials should be considered in trials where unique or specific treatment
effects may lead to unblinding individual patients.
If a double-blind trial is not feasible, then the single-blind option should be
considered. In some cases only an open-label trial is practically or ethically possible.
Single-blind and open-label trials provide additional flexibility, but it is particularly
important that the investigator's knowledge of the next treatment should not
influence the decision to enter the subject; this decision should precede knowledge of
the randomised treatment. For these trials, consideration should be given to the use
of a centralised randomisation method, such as telephone randomisation, to
administer the assignment of randomised treatment. In addition, clinical assessments
should be made by medical staff who are not involved in treating the subjects and
who remain blind to treatment. In single-blind or open-label trials every effort should
be made to minimise the various known sources of bias and primary variables should
be as objective as possible. The reasons for the degree of blinding adopted should be
explained in the protocol, together with steps taken to minimise bias by other means.
For example, the sponsor should have adequate standard operating procedures to
ensure that access to the treatment code is appropriately restricted during the process
of cleaning the database prior to its release for analysis.
Breaking the blind (for a single subject) should be considered only when knowledge of
the treatment assignment is deemed essential by the subject’s physician for the
subject’s care. Any intentional or unintentional breaking of the blind should be
reported and explained at the end of the trial, irrespective of the reason for its
occurrence. The procedure and timing for revealing the treatment assignments should
be documented.
In this document, the blind review (see Glossary) of data refers to the checking of data
during the period of time between trial completion (the last observation on the last
subject) and the breaking of the blind.
2.3.2 Randomisation
Randomisation introduces a deliberate element of chance into the assignment of
treatments to subjects in a clinical trial. During subsequent analysis of the trial data,
it provides a sound statistical basis for the quantitative evaluation of the evidence
relating to treatment effects. It also tends to produce treatment groups in which the
distributions of prognostic factors, known and unknown, are similar. In combination
with blinding, randomisation helps to avoid possible bias in the selection and
allocation of subjects arising from the predictability of treatment assignments.
The randomisation schedule of a clinical trial documents the random allocation of
treatments to subjects. In the simplest situation it is a sequential list of treatments
(or treatment sequences in a crossover trial) or corresponding codes by subject
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number. The logistics of some trials, such as those with a screening phase, may make
matters more complicated, but the unique pre-planned assignment of treatment, or
treatment sequence, to subject should be clear. Different trial designs will require
different procedures for generating randomisation schedules. The randomisation
schedule should be reproducible (if the need arises).
Although unrestricted randomisation is an acceptable approach, some advantages can
generally be gained by randomising subjects in blocks. This helps to increase the
comparability of the treatment groups, particularly when subject characteristics may
change over time, as a result, for example, of changes in recruitment policy. It also
provides a better guarantee that the treatment groups will be of nearly equal size. In
crossover trials it provides the means of obtaining balanced designs with their greater
efficiency and easier interpretation. Care should be taken to choose block lengths that
are sufficiently short to limit possible imbalance, but that are long enough to avoid
predictability towards the end of the sequence in a block. Investigators and other
relevant staff should generally be blind to the block length; the use of two or more
block lengths, randomly selected for each block, can achieve the same purpose.
(Theoretically, in a double-blind trial predictability does not matter, but the
pharmacological effects of drugs may provide the opportunity for intelligent
guesswork.)
In multicentre trials (see Glossary) the randomisation procedures should be organised
centrally. It is advisable to have a separate random scheme for each centre, i.e. to
stratify by centre or to allocate several whole blocks to each centre. More generally,
stratification by important prognostic factors measured at baseline (e.g. severity of
disease, age, sex, etc.) may sometimes be valuable in order to promote balanced
allocation within strata; this has greater potential benefit in small trials. The use of
more than two or three stratification factors is rarely necessary, is less successful at
achieving balance and is logistically troublesome. The use of a dynamic allocation
procedure (see below) may help to achieve balance across a number of stratification
factors simultaneously provided the rest of the trial procedures can be adjusted to
accommodate an approach of this type. Factors on which randomisation has been
stratified should be accounted for later in the analysis.
The next subject to be randomised into a trial should always receive the treatment
corresponding to the next free number in the appropriate randomisation schedule (in
the respective stratum, if randomisation is stratified). The appropriate number and
associated treatment for the next subject should only be allocated when entry of that
subject to the randomised part of the trial has been confirmed. Details of the
randomisation that facilitate predictability (e.g. block length) should not be contained
in the trial protocol. The randomisation schedule itself should be filed securely by the
sponsor or an independent party in a manner that ensures that blindness is properly
maintained throughout the trial. Access to the randomisation schedule during the
trial should take into account the possibility that, in an emergency, the blind may
have to be broken for any subject. The procedure to be followed, the necessary
documentation, and the subsequent treatment and assessment of the subject should
all be described in the protocol.
Dynamic allocation is an alternative procedure in which the allocation of treatment to
a subject is influenced by the current balance of allocated treatments and, in a
stratified trial, by the stratum to which the subject belongs and the balance within
that stratum. Deterministic dynamic allocation procedures should be avoided and an
appropriate element of randomisation should be incorporated for each treatment
allocation. Every effort should be made to retain the double-blind status of the trial.
For example, knowledge of the treatment code may be restricted to a central trial
office from where the dynamic allocation is controlled, generally through telephone
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contact. This in turn permits additional checks of eligibility criteria and establishes
entry into the trial, features that can be valuable in certain types of multicentre trial.
The usual system of pre-packing and labelling drug supplies for double-blind trials
can then be followed, but the order of their use is no longer sequential. It is desirable
to use appropriate computer algorithms to keep personnel at the central trial office
blind to the treatment code. The complexity of the logistics and potential impact on
the analysis should be carefully evaluated when considering dynamic allocation.
III. TRIAL DESIGN CONSIDERATIONS
3.1 Design Configuration
3.1.1 Parallel Group Design
The most common clinical trial design for confirmatory trials is the parallel group
design in which subjects are randomised to one of two or more arms, each arm being
allocated a different treatment. These treatments will include the investigational
product at one or more doses, and one or more control treatments, such as placebo
and/or an active comparator. The assumptions underlying this design are less
complex than for most other designs. However, as with other designs, there may be
additional features of the trial that complicate the analysis and interpretation (e.g.
covariates, repeated measurements over time, interactions between design factors,
protocol violations, dropouts (see Glossary) and withdrawals).
3.1.2 Crossover Design
In the crossover design, each subject is randomised to a sequence of two or more
treatments, and hence acts as his own control for treatment comparisons. This simple
manoeuvre is attractive primarily because it reduces the number of subjects and
usually the number of assessments needed to achieve a specific power, sometimes to a
marked extent. In the simplest 2×2 crossover design each subject receives each of two
treatments in randomised order in two successive treatment periods, often separated
by a washout period. The most common extension of this entails comparing n(>2)
treatments in n periods, each subject receiving all n treatments. Numerous variations
exist, such as designs in which each subject receives a subset of n(>2) treatments, or
ones in which treatments are repeated within a subject.
Crossover designs have a number of problems that can invalidate their results. The
chief difficulty concerns carryover, that is, the residual influence of treatments in
subsequent treatment periods. In an additive model the effect of unequal carryover
will be to bias direct treatment comparisons. In the 2×2 design the carryover effect
cannot be statistically distinguished from the interaction between treatment and
period and the test for either of these effects lacks power because the corresponding
contrast is 'between subject'. This problem is less acute in higher order designs, but
cannot be entirely dismissed.
When the crossover design is used it is therefore important to avoid carryover. This is
best done by selective and careful use of the design on the basis of adequate
knowledge of both the disease area and the new medication. The disease under study
should be chronic and stable. The relevant effects of the medication should develop
fully within the treatment period. The washout periods should be sufficiently long for
complete reversibility of drug effect. The fact that these conditions are likely to be met
should be established in advance of the trial by means of prior information and data.
There are additional problems that need careful attention in crossover trials. The
most notable of these are the complications of analysis and interpretation arising
from the loss of subjects. Also, the potential for carryover leads to difficulties in
assigning adverse events which occur in later treatment periods to the appropriate
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treatment. These, and other issues, are described in ICH E4. The crossover design
should generally be restricted to situations where losses of subjects from the trial are
expected to be small.
A common, and generally satisfactory, use of the 2×2 crossover design is to
demonstrate the bioequivalence of two formulations of the same medication. In this
particular application in healthy volunteers, carryover effects on the relevant
pharmacokinetic variable are most unlikely to occur if the wash-out time between the
two periods is sufficiently long. However it is still important to check this assumption
during analysis on the basis of the data obtained, for example by demonstrating that
no drug is detectable at the start of each period.
3.1.3 Factorial Designs
In a factorial design two or more treatments are evaluated simultaneously through
the use of varying combinations of the treatments. The simplest example is the 2×2
factorial design in which subjects are randomly allocated to one of the four possible
combinations of two treatments, A and B say. These are: A alone; B alone; both A and
B; neither A nor B. In many cases this design is used for the specific purpose of
examining the interaction of A and B. The statistical test of interaction may lack
power to detect an interaction if the sample size was calculated based on the test for
main effects. This consideration is important when this design is used for examining
the joint effects of A and B, in particular, if the treatments are likely to be used
together.
Another important use of the factorial design is to establish the dose-response
characteristics of the simultaneous use of treatments C and D, especially when the
efficacy of each monotherapy has been established at some dose in prior trials. A
number, m, of doses of C is selected, usually including a zero dose (placebo), and a
similar number, n, of doses of D. The full design then consists of m×n treatment
groups, each receiving a different combination of doses of C and D. The resulting
estimate of the response surface may then be used to help to identify an appropriate
combination of doses of C and D for clinical use (see ICH E4).
In some cases, the 2×2 design may be used to make efficient use of clinical trial
subjects by evaluating the efficacy of the two treatments with the same number of
subjects as would be required to evaluate the efficacy of either one alone. This
strategy has proved to be particularly valuable for very large mortality trials. The
efficiency and validity of this approach depends upon the absence of interaction
between treatments A and B so that the effects of A and B on the primary efficacy
variables follow an additive model, and hence the effect of A is virtually identical
whether or not it is additional to the effect of B. As for the crossover trial, evidence
that this condition is likely to be met should be established in advance of the trial by
means of prior information and data.
3.2 Multicentre Trials
Multicentre trials are carried out for two main reasons. Firstly, a multicentre trial is
an accepted way of evaluating a new medication more efficiently; under some
circumstances, it may present the only practical means of accruing sufficient subjects
to satisfy the trial objective within a reasonable time-frame. Multicentre trials of this
nature may, in principle, be carried out at any stage of clinical development. They
may have several centres with a large number of subjects per centre or, in the case of
a rare disease, they may have a large number of centres with very few subjects per
centre.
Secondly, a trial may be designed as a multicentre (and multi-investigator) trial
primarily to provide a better basis for the subsequent generalisation of its findings.
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This arises from the possibility of recruiting the subjects from a wider population and
of administering the medication in a broader range of clinical settings, thus
presenting an experimental situation that is more typical of future use. In this case
the involvement of a number of investigators also gives the potential for a wider
range of clinical judgement concerning the value of the medication. Such a trial would
be a confirmatory trial in the later phases of drug development and would be likely to
involve a large number of investigators and centres. It might sometimes be conducted
in a number of different countries in order to facilitate generalisability (see Glossary)
even further.
If a multicentre trial is to be meaningfully interpreted and extrapolated, then the
manner in which the protocol is implemented should be clear and similar at all
centres. Furthermore the usual sample size and power calculations depend upon the
assumption that the differences between the compared treatments in the centres are
unbiased estimates of the same quantity. It is important to design the common
protocol and to conduct the trial with this background in mind. Procedures should be
standardised as completely as possible. Variation of evaluation criteria and schemes
can be reduced by investigator meetings, by the training of personnel in advance of
the trial and by careful monitoring during the trial. Good design should generally aim
to achieve the same distribution of subjects to treatments within each centre and good
management should maintain this design objective. Trials that avoid excessive
variation in the numbers of subjects per centre and trials that avoid a few very small
centres have advantages if it is later found necessary to take into account the
heterogeneity of the treatment effect from centre to centre, because they reduce the
differences between different weighted estimates of the treatment effect. (This point
does not apply to trials in which all centres are very small and in which centre does
not feature in the analysis.) Failure to take these precautions, combined with doubts
about the homogeneity of the results may, in severe cases, reduce the value of a
multicentre trial to such a degree that it cannot be regarded as giving convincing
evidence for the sponsor’s claims.
In the simplest multicentre trial, each investigator will be responsible for the subjects
recruited at one hospital, so that ‘centre’ is identified uniquely by either investigator
or hospital. In many trials, however, the situation is more complex. One investigator
may recruit subjects from several hospitals; one investigator may represent a team of
clinicians (subinvestigators) who all recruit subjects from their own clinics at one
hospital or at several associated hospitals. Whenever there is room for doubt about
the definition of centre in a statistical model, the statistical section of the protocol (see
Section 5.1) should clearly define the term (e.g. by investigator, location or region) in
the context of the particular trial. In most instances centres can be satisfactorily
defined through the investigators and ICH E6 provides relevant guidance in this
respect. In cases of doubt the aim should be to define centres so as to achieve
homogeneity in the important factors affecting the measurements of the primary
variables and the influence of the treatments. Any rules for combining centres in the
analysis should be justified and specified prospectively in the protocol where possible,
but in any case decisions concerning this approach should always be taken blind to
treatment, for example at the time of the blind review.
The statistical model to be adopted for the estimation and testing of treatment effects
should be described in the protocol. The main treatment effect may be investigated
first using a model which allows for centre differences, but does not include a term for
treatment-by-centre interaction. If the treatment effect is homogeneous across
centres, the routine inclusion of interaction terms in the model reduces the efficiency
of the test for the main effects. In the presence of true heterogeneity of treatment
effects, the interpretation of the main treatment effect is controversial.
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In some trials, for example some large mortality trials with very few subjects per
centre, there may be no reason to expect the centres to have any influence on the
primary or secondary variables because they are unlikely to represent influences of
clinical importance. In other trials it may be recognised from the start that the
limited numbers of subjects per centre will make it impracticable to include the centre
effects in the statistical model. In these cases it is not appropriate to include a term
for centre in the model, and it is not necessary to stratify the randomisation by centre
in this situation.
If positive treatment effects are found in a trial with appreciable numbers of subjects
per centre, there should generally be an exploration of the heterogeneity of treatment
effects across centres, as this may affect the generalisability of the conclusions.
Marked heterogeneity may be identified by graphical display of the results of
individual centres or by analytical methods, such as a significance test of the
treatment-by-centre interaction. When using such a statistical significance test, it is
important to recognise that this generally has low power in a trial designed to detect
the main effect of treatment.
If heterogeneity of treatment effects is found, this should be interpreted with care and
vigorous attempts should be made to find an explanation in terms of other features of
trial management or subject characteristics. Such an explanation will usually suggest
appropriate further analysis and interpretation. In the absence of an explanation,
heterogeneity of treatment effect as evidenced, for example, by marked quantitative
interactions (see Glossary) implies that alternative estimates of the treatment effect
may be required, giving different weights to the centres, in order to substantiate the
robustness of the estimates of treatment effect. It is even more important to
understand the basis of any heterogeneity characterised by marked qualitative
interactions (see Glossary), and failure to find an explanation may necessitate further
clinical trials before the treatment effect can be reliably predicted.
Up to this point the discussion of multicentre trials has been based on the use of fixed
effect models. Mixed models may also be used to explore the heterogeneity of the
treatment effect. These models consider centre and treatment-by-centre effects to be
random, and are especially relevant when the number of sites is large.
3.3 Type of Comparison
3.3.1 Trials to Show Superiority
Scientifically, efficacy is most convincingly established by demonstrating superiority
to placebo in a placebo-controlled trial, by showing superiority to an active control
treatment or by demonstrating a dose-response relationship. This type of trial is
referred to as a ‘superiority’ trial (see Glossary). Generally in this guidance
superiority trials are assumed, unless it is explicitly stated otherwise.
For serious illnesses, when a therapeutic treatment which has been shown to be
efficacious by superiority trial(s) exists, a placebo-controlled trial may be considered
unethical. In that case the scientifically sound use of an active treatment as a control
should be considered. The appropriateness of placebo control vs. active control should
be considered on a trial by trial basis.
3.3.2 Trials to Show Equivalence or Non-inferiority
In some cases, an investigational product is compared to a reference treatment
without the objective of showing superiority. This type of trial is divided into two
major categories according to its objective; one is an 'equivalence' trial (see Glossary)
and the other is a 'non-inferiority' trial (see Glossary).
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Bioequivalence trials fall into the former category. In some situations, clinical
equivalence trials are also undertaken for other regulatory reasons such as
demonstrating the clinical equivalence of a generic product to the marketed product
when the compound is not absorbed and therefore not present in the blood stream.
Many active control trials are designed to show that the efficacy of an investigational
product is no worse than that of the active comparator, and hence fall into the latter
category. Another possibility is a trial in which multiple doses of the investigational
drug are compared with the recommended dose or multiple doses of the standard
drug. The purpose of this design is simultaneously to show a dose-response
relationship for the investigational product and to compare the investigational
product with the active control.
Active control equivalence or non-inferiority trials may also incorporate a placebo,
thus pursuing multiple goals in one trial; for example, they may establish superiority
to placebo and hence validate the trial design and simultaneously evaluate the degree
of similarity of efficacy and safety to the active comparator. There are well known
difficulties associated with the use of the active control equivalence (or non-inferiority)
trials that do not incorporate a placebo or do not use multiple doses of the new drug.
These relate to the implicit lack of any measure of internal validity (in contrast to
superiority trials), thus making external validation necessary. The equivalence (or
non-inferiority) trial is not conservative in nature, so that many flaws in the design or
conduct of the trial will tend to bias the results towards a conclusion of equivalence.
For these reasons, the design features of such trials should receive special attention
and their conduct needs special care. For example, it is especially important to
minimise the incidence of violations of the entry criteria, non-compliance,
withdrawals, losses to follow-up, missing data and other deviations from the protocol,
and also to minimise their impact on the subsequent analyses.
Active comparators should be chosen with care. An example of a suitable active
comparator would be a widely used therapy whose efficacy in the relevant indication
has been clearly established and quantified in well designed and well documented
superiority trial(s) and which can be reliably expected to exhibit similar efficacy in the
contemplated active control trial. To this end, the new trial should have the same
important design features (primary variables, the dose of the active comparator,
eligibility criteria, etc.) as the previously conducted superiority trials in which the
active comparator clearly demonstrated clinically relevant efficacy, taking into
account advances in medical or statistical practice relevant to the new trial.
It is vital that the protocol of a trial designed to demonstrate equivalence or noninferiority contain a clear statement that this is its explicit intention. An equivalence
margin should be specified in the protocol; this margin is the largest difference that
can be judged as being clinically acceptable and should be smaller than differences
observed in superiority trials of the active comparator. For the active control
equivalence trial, both the upper and the lower equivalence margins are needed,
while only the lower margin is needed for the active control non-inferiority trial. The
choice of equivalence margins should be justified clinically.
Statistical analysis is generally based on the use of confidence intervals (see Section
5.5). For equivalence trials, two-sided confidence intervals should be used.
Equivalence is inferred when the entire confidence interval falls within the
equivalence margins. Operationally, this is equivalent to the method of using two
simultaneous one-sided tests to test the (composite) null hypothesis that the
treatment difference is outside the equivalence margins versus the (composite)
alternative hypothesis that the treatment difference is within the margins. Because
the two null hypotheses are disjoint, the type I error is appropriately controlled. For
15
non-inferiority trials a one-sided interval should be used. The confidence interval
approach has a one-sided hypothesis test counterpart for testing the null hypothesis
that the treatment difference (investigational product minus control) is equal to the
lower equivalence margin versus the alternative that the treatment difference is
greater than the lower equivalence margin. The choice of type I error should be a
consideration separate from the use of a one-sided or two-sided procedure. Sample
size calculations should be based on these methods (see Section 3.5).
Concluding equivalence or non-inferiority based on observing a non-significant test
result of the null hypothesis that there is no difference between the investigational
product and the active comparator is inappropriate.
There are also special issues in the choice of analysis sets. Subjects who withdraw or
dropout of the treatment group or the comparator group will tend to have a lack of
response, and hence the results of using the full analysis set (see Glossary) may be
biased toward demonstrating equivalence (see Section 5.2.3).
3.3.3 Trials to Show Dose-response Relationship
How response is related to the dose of a new investigational product is a question to
which answers may be obtained in all phases of development, and by a variety of
approaches (see ICH E4). Dose-response trials may serve a number of objectives,
amongst which the following are of particular importance: the confirmation of efficacy;
the investigation of the shape and location of the dose-response curve; the estimation
of an appropriate starting dose; the identification of optimal strategies for individual
dose adjustments; the determination of a maximal dose beyond which additional
benefit would be unlikely to occur. These objectives should be addressed using the
data collected at a number of doses under investigation, including a placebo (zero
dose) wherever appropriate. For this purpose the application of procedures to estimate
the relationship between dose and response, including the construction of confidence
intervals and the use of graphical methods, is as important as the use of statistical
tests. The hypothesis tests that are used may need to be tailored to the natural
ordering of doses or to particular questions regarding the shape of the dose-response
curve (e.g. monotonicity). The details of the planned statistical procedures should be
given in the protocol.
3.4 Group Sequential Designs
Group sequential designs are used to facilitate the conduct of interim analysis (see
section 4.5 and Glossary). While group sequential designs are not the only acceptable
types of designs permitting interim analysis, they are the most commonly applied
because it is more practicable to assess grouped subject outcomes at periodic intervals
during the trial than on a continuous basis as data from each subject become
available. The statistical methods should be fully specified in advance of the
availability of information on treatment outcomes and subject treatment assignments
(i.e. blind breaking, see Section 4.5). An Independent Data Monitoring Committee
(see Glossary) may be used to review or to conduct the interim analysis of data arising
from a group sequential design (see Section 4.6). While the design has been most
widely and successfully used in large, long-term trials of mortality or major non-fatal
endpoints, its use is growing in other circumstances. In particular, it is recognised
that safety must be monitored in all trials and therefore the need for formal
procedures to cover early stopping for safety reasons should always be considered.
3.5 Sample Size
The number of subjects in a clinical trial should always be large enough to provide a
reliable answer to the questions addressed. This number is usually determined by the
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primary objective of the trial. If the sample size is determined on some other basis,
then this should be made clear and justified. For example, a trial sized on the basis of
safety questions or requirements or important secondary objectives may need larger
numbers of subjects than a trial sized on the basis of the primary efficacy question
(see, for example, ICH E1a).
Using the usual method for determining the appropriate sample size, the following
items should be specified: a primary variable, the test statistic, the null hypothesis,
the alternative ('working') hypothesis at the chosen dose(s) (embodying consideration
of the treatment difference to be detected or rejected at the dose and in the subject
population selected), the probability of erroneously rejecting the null hypothesis (the
type I error), and the probability of erroneously failing to reject the null hypothesis
(the type II error), as well as the approach to dealing with treatment withdrawals and
protocol violations. In some instances, the event rate is of primary interest for
evaluating power, and assumptions should be made to extrapolate from the required
number of events to the eventual sample size for the trial.
The method by which the sample size is calculated should be given in the protocol,
together with the estimates of any quantities used in the calculations (such as
variances, mean values, response rates, event rates, difference to be detected). The
basis of these estimates should also be given. It is important to investigate the
sensitivity of the sample size estimate to a variety of deviations from these
assumptions and this may be facilitated by providing a range of sample sizes
appropriate for a reasonable range of deviations from assumptions. In confirmatory
trials, assumptions should normally be based on published data or on the results of
earlier trials. The treatment difference to be detected may be based on a judgement
concerning the minimal effect which has clinical relevance in the management of
patients or on a judgement concerning the anticipated effect of the new treatment,
where this is larger. Conventionally the probability of type I error is set at 5% or less
or as dictated by any adjustments made necessary for multiplicity considerations; the
precise choice may be influenced by the prior plausibility of the hypothesis under test
and the desired impact of the results. The probability of type II error is conventionally
set at 10% to 20%; it is in the sponsor’s interest to keep this figure as low as feasible
especially in the case of trials that are difficult or impossible to repeat. Alternative
values to the conventional levels of type I and type II error may be acceptable or even
preferable in some cases.
Sample size calculations should refer to the number of subjects required for the
primary analysis. If this is the 'full analysis set', estimates of the effect size may need
to be reduced compared to the per protocol set (see Glossary). This is to allow for the
dilution of the treatment effect arising from the inclusion of data from patients who
have withdrawn from treatment or whose compliance is poor. The assumptions about
variability may also need to be revised.
The sample size of an equivalence trial or a non-inferiority trial (see Section 3.3.2)
should normally be based on the objective of obtaining a confidence interval for the
treatment difference that shows that the treatments differ at most by a clinically
acceptable difference. When the power of an equivalence trial is assessed at a true
difference of zero, then the sample size necessary to achieve this power is
underestimated if the true difference is not zero. When the power of a non-inferiority
trial is assessed at a zero difference, then the sample size needed to achieve that
power will be underestimated if the effect of the investigational product is less than
that of the active control. The choice of a 'clinically acceptable’ difference needs
justification with respect to its meaning for future patients, and may be smaller than
the 'clinically relevant' difference referred to above in the context of superiority trials
designed to establish that a difference exists.
17
The exact sample size in a group sequential trial cannot be fixed in advance because
it depends upon the play of chance in combination with the chosen stopping guideline
and the true treatment difference. The design of the stopping guideline should take
into account the consequent distribution of the sample size, usually embodied in the
expected and maximum sample sizes.
When event rates are lower than anticipated or variability is larger than expected,
methods for sample size re-estimation are available without unblinding data or
making treatment comparisons (see Section 4.4).
3.6 Data Capture and Pr ocessing
The collection of data and transfer of data from the investigator to the sponsor can
take place through a variety of media, including paper case record forms, remote site
monitoring systems, medical computer systems and electronic transfer. Whatever
data capture instrument is used, the form and content of the information collected
should be in full accordance with the protocol and should be established in advance of
the conduct of the clinical trial. It should focus on the data necessary to implement
the planned analysis, including the context information (such as timing assessments
relative to dosing) necessary to confirm protocol compliance or identify important
protocol deviations. ‘Missing values’ should be distinguishable from the ‘value zero’ or
‘characteristic absent’.
The process of data capture through to database finalisation should be carried out in
accordance with GCP (see ICH E6, Section 5). Specifically, timely and reliable
processes for recording data and rectifying errors and omissions are necessary to
ensure delivery of a quality database and the achievement of the trial objectives
through the implementation of the planned analysis.
IV. TRIAL CONDUCT CONSIDERATIONS
4.1 Trial Monitoring and Interim Analysis
Careful conduct of a clinical trial according to the protocol has a major impact on the
credibility of the results (see ICH E6). Careful monitoring can ensure that difficulties
are noticed early and their occurrence or recurrence minimised.
There are two distinct types of monitoring that generally characterise confirmatory
clinical trials sponsored by the pharmaceutical industry. One type of monitoring
concerns the oversight of the quality of the trial, while the other type involves
breaking the blind to make treatment comparisons (i.e. interim analysis). Both types
of trial monitoring, in addition to entailing different staff responsibilities, involve
access to different types of trial data and information, and thus different principles
apply for the control of potential statistical and operational bias.
For the purpose of overseeing the quality of the trial the checks involved in trial
monitoring may include whether the protocol is being followed, the acceptability of
data being accrued, the success of planned accrual targets, the appropriateness of the
design assumptions, success in keeping patients in the trials, etc. (see Sections 4.2 to
4.4). This type of monitoring does not require access to information on comparative
treatment effects, nor unblinding of data and therefore has no impact on type I error.
The monitoring of a trial for this purpose is the responsibility of the sponsor (see ICH
E6) and can be carried out by the sponsor or an independent group selected by the
sponsor. The period for this type of monitoring usually starts with the selection of the
trial sites and ends with the collection and cleaning of the last subject’s data.
The other type of trial monitoring (interim analysis) involves the accruing of
comparative treatment results. Interim analysis requires unblinded (i.e. key
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breaking) access to treatment group assignment (actual treatment assignment or
identification of group assignment) and comparative treatment group summary
information. This necessitates that the protocol (or appropriate amendments prior to
a first analysis) contains statistical plans for the interim analysis to prevent certain
types of bias. This is discussed in Sections 4.5 & 4.6.
4.2 Changes in Inclusion and Exclusion Criteria
Inclusion and exclusion criteria should remain constant, as specified in the protocol,
throughout the period of subject recruitment. Changes may occasionally be
appropriate, for example, in long term trials, where growing medical knowledge either
from outside the trial or from interim analyses may suggest a change of entry criteria.
Changes may also result from the discovery by monitoring staff that regular
violations of the entry criteria are occurring, or that seriously low recruitment rates
are due to over-restrictive criteria. Changes should be made without breaking the
blind and should always be described by a protocol amendment which should cover
any statistical consequences, such as sample size adjustments arising from different
event rates, or modifications to the planned analysis, such as stratifying the analysis
according to modified inclusion/exclusion criteria.
4.3 Accrual Rates
In trials with a long time-scale for the accrual of subjects, the rate of accrual should
be monitored and, if it falls appreciably below the projected level, the reasons should
be identified and remedial actions taken in order to protect the power of the trial and
alleviate concerns about selective entry and other aspects of quality. In a multicentre
trial these considerations apply to the individual centres.
4.4 Sample Size Adjustment
In long term trials there will usually be an opportunity to check the assumptions
which underlay the original design and sample size calculations. This may be
particularly important if the trial specifications have been made on preliminary
and/or uncertain information. An interim check conducted on the blinded data may
reveal that overall response variances, event rates or survival experience are not as
anticipated. A revised sample size may then be calculated using suitably modified
assumptions, and should be justified and documented in a protocol amendment and in
the clinical study report. The steps taken to preserve blindness and the consequences,
if any, for the type I error and the width of confidence intervals should be explained.
The potential need for re-estimation of the sample size should be envisaged in the
protocol whenever possible (see Section 3.5).
4.5 Interim Analysis and Early Stopping
An interim analysis is any analysis intended to compare treatment arms with respect
to efficacy or safety at any time prior to formal completion of a trial. Because the
number, methods and consequences of these comparisons affect the interpretation of
the trial, all interim analyses should be carefully planned in advance and described in
the protocol. Special circumstances may dictate the need for an interim analysis that
was not defined at the start of a trial. In these cases, a protocol amendment
describing the interim analysis should be completed prior to unblinded access to
treatment comparison data. When an interim analysis is planned with the intention
of deciding whether or not to terminate a trial, this is usually accomplished by the use
of a group sequential design which employs statistical monitoring schemes as
guidelines (see Section 3.4). The goal of such an interim analysis is to stop the trial
early if the superiority of the treatment under study is clearly established, if the
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demonstration of a relevant treatment difference has become unlikely or if
unacceptable adverse effects are apparent. Generally, boundaries for monitoring
efficacy require more evidence to terminate a trial early (i.e. they are more
conservative) than boundaries for monitoring safety. When the trial design and
monitoring objective involve multiple endpoints then this aspect of multiplicity may
also need to be taken into account.
The protocol should describe the schedule of interim analyses, or at least the
considerations which will govern its generation, for example if flexible alpha spending
function approaches are to be employed; further details may be given in a protocol
amendment before the time of the first interim analysis. The stopping guidelines and
their properties should be clearly described in the protocol or amendments. The
potential effects of early stopping on the analysis of other important variables should
also be considered. This material should be written or approved by the Data
Monitoring Committee (see Section 4.6), when the trial has one. Deviations from the
planned procedure always bear the potential of invalidating the trial results. If it
becomes necessary to make changes to the trial, any consequent changes to the
statistical procedures should be specified in an amendment to the protocol at the
earliest opportunity, especially discussing the impact on any analysis and inferences
that such changes may cause. The procedures selected should always ensure that the
overall probability of type I error is controlled.
The execution of an interim analysis should be a completely confidential process
because unblinded data and results are potentially involved. All staff involved in the
conduct of the trial should remain blind to the results of such analyses, because of the
possibility that their attitudes to the trial will be modified and cause changes in the
characteristics of patients to be recruited or biases in treatment comparisons. This
principle may be applied to all investigator staff and to staff employed by the sponsor
except for those who are directly involved in the execution of the interim analysis.
Investigators should only be informed about the decision to continue or to discontinue
the trial, or to implement modifications to trial procedures.
Most clinical trials intended to support the efficacy and safety of an investigational
product should proceed to full completion of planned sample size accrual; trials should
be stopped early only for ethical reasons or if the power is no longer acceptable.
However, it is recognised that drug development plans involve the need for sponsor
access to comparative treatment data for a variety of reasons, such as planning other
trials. It is also recognised that only a subset of trials will involve the study of serious
life-threatening outcomes or mortality which may need sequential monitoring of
accruing comparative treatment effects for ethical reasons. In either of these
situations, plans for interim statistical analysis should be in place in the protocol or in
protocol amendments prior to the unblinded access to comparative treatment data in
order to deal with the potential statistical and operational bias that may be
introduced.
For many clinical trials of investigational products, especially those that have major
public health significance, the responsibility for monitoring comparisons of efficacy
and/or safety outcomes should be assigned to an external independent group, often
called an Independent Data Monitoring Committee (IDMC), a Data and Safety
Monitoring Board or a Data Monitoring Committee whose responsibilities should be
clearly described.
When a sponsor assumes the role of monitoring efficacy or safety comparisons and
therefore has access to unblinded comparative information, particular care should be
taken to protect the integrity of the trial and to manage and limit appropriately the
sharing of information. The sponsor should assure and document that the internal
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monitoring committee has complied with written standard operating procedures and
that minutes of decision making meetings including records of interim results are
maintained.
Any interim analysis that is not planned appropriately (with or without the
consequences of stopping the trial early) may flaw the results of a trial and possibly
weaken confidence in the conclusions drawn. Therefore, such analyses should be
avoided. If unplanned interim analysis is conducted, the clinical study report should
explain why it was necessary, the degree to which blindness had to be broken, provide
an assessment of the potential magnitude of bias introduced, and the impact on the
interpretation of the results.
4.6 Role of Independent Data Monitoring Committee (IDMC)
(see Sections 1.25 and 5.52 of ICH E6)
An IDMC may be established by the sponsor to assess at intervals the progress of a
clinical trial, safety data, and critical efficacy variables and recommend to the sponsor
whether to continue, modify or terminate a trial. The IDMC should have written
operating procedures and maintain records of all its meetings, including interim
results; these should be available for review when the trial is complete. The
independence of the IDMC is intended to control the sharing of important
comparative information and to protect the integrity of the clinical trial from adverse
impact resulting from access to trial information. The IDMC is a separate entity from
an Institutional Review Board (IRB) or an Independent Ethics Committee (IEC), and
its composition should include clinical trial scientists knowledgeable in the
appropriate disciplines including statistics.
When there are sponsor representatives on the IDMC, their role should be clearly
defined in the operating procedures of the committee (for example, covering whether
or not they can vote on key issues). Since these sponsor staff would have access to
unblinded information, the procedures should also address the control of
dissemination of interim trial results within the sponsor organisation.
V. DATA ANALYSIS CONSIDERATIONS
5.1 Prespecification of the Analysis
When designing a clinical trial the principal features of the eventual statistical
analysis of the data should be described in the statistical section of the protocol. This
section should include all the principal features of the proposed confirmatory analysis
of the primary variable(s) and the way in which anticipated analysis problems will be
handled. In case of exploratory trials this section could describe more general
principles and directions.
The statistical analysis plan (see Glossary) may be written as a separate document to
be completed after finalising the protocol. In this document, a more technical and
detailed elaboration of the principal features stated in the protocol may be included
(see section 7.1). The plan may include detailed procedures for executing the
statistical analysis of the primary and secondary variables and other data. The plan
should be reviewed and possibly updated as a result of the blind review of the data
(see 7.1 for definition) and should be finalised before breaking the blind. Formal
records should be kept of when the statistical analysis plan was finalised as well as
when the blind was subsequently broken.
If the blind review suggests changes to the principal features stated in the protocol,
these should be documented in a protocol amendment. Otherwise, it will suffice to
update the statistical analysis plan with the considerations suggested from the blind
21
review. Only results from analyses envisaged in the protocol (including amendments)
can be regarded as confirmatory.
In the statistical section of the clinical study report the statistical methodology should
be clearly described including when in the clinical trial process methodology decisions
were made (see ICH E3).
5.2 Analysis Sets
The set of subjects whose data are to be included in the main analyses should be
defined in the statistical section of the protocol. In addition, documentation for all
subjects for whom trial procedures (e.g. run-in period) were initiated may be useful.
The content of this subject documentation depends on detailed features of the
particular trial, but at least demographic and baseline data on disease status should
be collected whenever possible.
If all subjects randomised into a clinical trial satisfied all entry criteria, followed all
trial procedures perfectly with no losses to follow-up, and provided complete data
records, then the set of subjects to be included in the analysis would be self-evident.
The design and conduct of a trial should aim to approach this ideal as closely as
possible, but, in practice, it is doubtful if it can ever be fully achieved. Hence, the
statistical section of the protocol should address anticipated problems prospectively in
terms of how these affect the subjects and data to be analysed. The protocol should
also specify procedures aimed at minimising any anticipated irregularities in study
conduct that might impair a satisfactory analysis, including various types of protocol
violations, withdrawals and missing values. The protocol should consider ways both to
reduce the frequency of such problems, and also to handle the problems that do occur
in the analysis of data. Possible amendments to the way in which the analysis will
deal with protocol violations should be identified during the blind review. It is
desirable to identify any important protocol violation with respect to the time when it
occurred, its cause and influence on the trial result. The frequency and type of
protocol violations, missing values, and other problems should be documented in the
clinical study report and their potential influence on the trial results should be
described (see ICH E3).
Decisions concerning the analysis set should be guided by the following principles : 1)
to minimise bias, and 2) to avoid inflation of type I error.
5.2.1 Full Analysis Set
The intention-to-treat (see Glossary) principle implies that the primary analysis
should include all randomised subjects. Compliance with this principle would
necessitate complete follow-up of all randomised subjects for study outcomes. In
practice this ideal may be difficult to achieve, for reasons to be described. In this
document the term 'full analysis set' is used to describe the analysis set which is as
complete as possible and as close as possible to the intention-to-treat ideal of
including all randomised subjects. Preservation of the initial randomisation in
analysis is important in preventing bias and in providing a secure foundation for
statistical tests. In many clinical trials the use of the full analysis set provides a
conservative strategy. Under many circumstances it may also provide estimates of
treatment effects which are more likely to mirror those observed in subsequent
practice.
There are a limited number of circumstances that might lead to excluding randomised
subjects from the full analysis set including the failure to satisfy major entry criteria
(eligibility violations), the failure to take at least one dose of trial medication and the
lack of any data post randomisation. Such exclusions should always be justified.
22
Subjects who fail to satisfy an entry criterion may be excluded from the analysis
without the possibility of introducing bias only under the following circumstances:
(i) the entry criterion was measured prior to randomisation;
(ii) the detection of the relevant eligibility violations can be made completely
objectively;
(iii) all subjects receive equal scrutiny for eligibility violations; (This may be
difficult to ensure in an open-label study, or even in a double-blind study if
the data are unblinded prior to this scrutiny, emphasising the importance of
the blind review.)
(iv) all detected violations of the particular entry criterion are excluded.
In some situations, it may be reasonable to eliminate from the set of all randomised
subjects any subject who took no trial medication. The intention-to-treat principle
would be preserved despite the exclusion of these patients provided, for example, that
the decision of whether or not to begin treatment could not be influenced by
knowledge of the assigned treatment. In other situations it may be necessary to
eliminate from the set of all randomised subjects any subject without data post
randomisation. No analysis is complete unless the potential biases arising from these
specific exclusions, or any others, are addressed.
When the full analysis set of subjects is used, violations of the protocol that occur
after randomisation may have an impact on the data and conclusions, particularly if
their occurrence is related to treatment assignment. In most respects it is appropriate
to include the data from such subjects in the analysis, consistent with the intentionto-treat principle. Special problems arise in connection with subjects withdrawn from
treatment after receiving one or more doses who provide no data after this point, and
subjects otherwise lost to follow-up, because failure to include these subjects in the
full analysis set may seriously undermine the approach. Measurements of primary
variables made at the time of the loss to follow-up of a subject for any reason, or
subsequently collected in accordance with the intended schedule of assessments in the
protocol, are valuable in this context; subsequent collection is especially important in
studies where the primary variable is mortality or serious morbidity. The intention to
collect data in this way should be described in the protocol. Imputation techniques,
ranging from the carrying forward of the last observation to the use of complex
mathematical models, may also be used in an attempt to compensate for missing
data. Other methods employed to ensure the availability of measurements of primary
variables for every subject in the full analysis set may require some assumptions
about the subjects' outcomes or a simpler choice of outcome (e.g. success / failure). The
use of any of these strategies should be described and justified in the statistical
section of the protocol and the assumptions underlying any mathematical models
employed should be clearly explained. It is also important to demonstrate the
robustness of the corresponding results of analysis especially when the strategy in
question could itself lead to biased estimates of treatment effects.
Because of the unpredictability of some problems, it may sometimes be preferable to
defer detailed consideration of the manner of dealing with irregularities until the
blind review of the data at the end of the trial, and, if so, this should be stated in the
protocol.
5.2.2 Per Protocol Set
The 'per protocol' set of subjects, sometimes described as the 'valid cases', the 'efficacy'
sample or the 'evaluable subjects' sample, defines a subset of the subjects in the full
23
analysis set who are more compliant with the protocol and is characterised by criteria
such as the following:
(i) the completion of a certain pre-specified minimal exposure to the treatment
regimen;
(ii) the availability of measurements of the primary variable(s);
(iii) the absence of any major protocol violations including the violation of entry
criteria.
The precise reasons for excluding subjects from the per protocol set should be fully
defined and documented before breaking the blind in a manner appropriate to the
circumstances of the specific trial.
The use of the per protocol set may maximise the opportunity for a new treatment to
show additional efficacy in the analysis, and most closely reflects the scientific model
underlying the protocol. However, the corresponding test of the hypothesis and
estimate of the treatment effect may or may not be conservative depending on the
trial; the bias, which may be severe, arises from the fact that adherence to the study
protocol may be related to treatment and outcome.
The problems that lead to the exclusion of subjects to create the per protocol set, and
other protocol violations, should be fully identified and summarised. Relevant protocol
violations may include errors in treatment assignment, the use of excluded
medication, poor compliance, loss to follow-up and missing data. It is good practice to
assess the pattern of such problems among the treatment groups with respect to
frequency and time to occurrence.
5.2.3 Roles of the Different Analysis Sets
In general, it is advantageous to demonstrate a lack of sensitivity of the principal trial
results to alternative choices of the set of subjects analysed. In confirmatory trials it
is usually appropriate to plan to conduct both an analysis of the full analysis set and a
per protocol analysis, so that any differences between them can be the subject of
explicit discussion and interpretation. In some cases, it may be desirable to plan
further exploration of the sensitivity of conclusions to the choice of the set of subjects
analysed. When the full analysis set and the per protocol set lead to essentially the
same conclusions, confidence in the trial results is increased, bearing in mind,
however, that the need to exclude a substantial proportion of subjects from the per
protocol analysis throws some doubt on the overall validity of the trial.
The full analysis set and the per protocol set play different roles in superiority trials
(which seek to show the investigational product to be superior), and in equivalence or
non-inferiority trials (which seek to show the investigational product to be
comparable, see section 3.3.2). In superiority trials the full analysis set is used in the
primary analysis (apart from exceptional circumstances) because it tends to avoid
over-optimistic estimates of efficacy resulting from a per protocol analysis, since the
non-compliers included in the full analysis set will generally diminish the estimated
treatment effect. However, in an equivalence or non-inferiority trial use of the full
analysis set is generally not conservative and its role should be considered very
carefully.
5.3 Missing Values and Outliers
Missing values represent a potential source of bias in a clinical trial. Hence, every
effort should be undertaken to fulfil all the requirements of the protocol concerning
the collection and management of data. In reality, however, there will almost always
24
be some missing data. A trial may be regarded as valid, nonetheless, provided the
methods of dealing with missing values are sensible, and particularly if those
methods are pre-defined in the protocol. Definition of methods may be refined by
updating this aspect in the statistical analysis plan during the blind review.
Unfortunately, no universally applicable methods of handling missing values can be
recommended. An investigation should be made concerning the sensitivity of the
results of analysis to the method of handling missing values, especially if the number
of missing values is substantial.
A similar approach should be adopted to exploring the influence of outliers, the
statistical definition of which is, to some extent, arbitrary. Clear identification of a
particular value as an outlier is most convincing when justified medically as well as
statistically, and the medical context will then often define the appropriate action.
Any outlier procedure set out in the protocol or the statistical analysis plan should be
such as not to favour any treatment group a priori. Once again, this aspect of the
analysis can be usefully updated during blind review. If no procedure for dealing with
outliers was foreseen in the trial protocol, one analysis with the actual values and at
least one other analysis eliminating or reducing the outlier effect should be performed
and differences between their results discussed.
5.4 Data Transfor mation
The decision to transform key variables prior to analysis is best made during the
design of the trial on the basis of similar data from earlier clinical trials.
Transformations (e.g. square root, logarithm) should be specified in the protocol and a
rationale provided, especially for the primary variable(s). The general principles
guiding the use of transformations to ensure that the assumptions underlying the
statistical methods are met are to be found in standard texts; conventions for
particular variables have been developed in a number of specific clinical areas. The
decision on whether and how to transform a variable should be influenced by the
preference for a scale which facilitates clinical interpretation.
Similar considerations apply to other derived variables, such as the use of change
from baseline, percentage change from baseline, the 'area under the curve' of repeated
measures, or the ratio of two different variables. Subsequent clinical interpretation
should be carefully considered, and the derivation should be justified in the protocol.
Closely related points are made in Section 2.2.2.
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5.5 Estimation, Confidence Intervals and Hypothesis Testing
The statistical section of the protocol should specify the hypotheses that are to be
tested and/or the treatment effects which are to be estimated in order to satisfy the
primary objectives of the trial. The statistical methods to be used to accomplish these
tasks should be described for the primary (and preferably the secondary) variables,
and the underlying statistical model should be made clear. Estimates of treatment
effects should be accompanied by confidence intervals, whenever possible, and the
way in which these will be calculated should be identified. A description should be
given of any intentions to use baseline data to improve precision or to adjust
estimates for potential baseline differences, for example by means of analysis of
covariance.
It is important to clarify whether one- or two-sided tests of statistical significance will
be used, and in particular to justify prospectively the use of one-sided tests. If
hypothesis tests are not considered appropriate, then the alternative process for
arriving at statistical conclusions should be given. The issue of one-sided or two-sided
approaches to inference is controversial and a diversity of views can be found in the
statistical literature. The approach of setting type I errors for one-sided tests at half
the conventional type I error used in two-sided tests is preferable in regulatory
settings. This promotes consistency with the two-sided confidence intervals that are
generally appropriate for estimating the possible size of the difference between two
treatments.
The particular statistical model chosen should reflect the current state of medical and
statistical knowledge about the variables to be analysed as well as the statistical
design of the trial. All effects to be fitted in the analysis (for example in analysis of
variance models) should be fully specified, and the manner, if any, in which this set of
effects might be modified in response to preliminary results should be explained. The
same considerations apply to the set of covariates fitted in an analysis of covariance.
(See also Section 5.7.). In the choice of statistical methods due attention should be
paid to the statistical distribution of both primary and secondary variables. When
making this choice (for example between parametric and non-parametric methods) it
is important to bear in mind the need to provide statistical estimates of the size of
treatment effects together with confidence intervals (in addition to significance tests).
The primary analysis of the primary variable should be clearly distinguished from
supporting analyses of the primary or secondary variables. Within the statistical
section of the protocol or the statistical analysis plan there should also be an outline
of the way in which data other than the primary and secondary variables will be
summarised and reported. This should include a reference to any approaches adopted
for the purpose of achieving consistency of analysis across a range of trials, for
example for safety data.
Modelling approaches that incorporate information on known pharmacological
parameters, the extent of protocol compliance for individual subjects or other
biologically based data may provide valuable insights into actual or potential efficacy,
especially with regard to estimation of treatment effects. The assumptions underlying
such models should always be clearly identified, and the limitations of any conclusions
should be carefully described.
5.6 Adjustment of Significance and Confidence Levels
When multiplicity is present, the usual frequentist approach to the analysis of clinical
trial data may necessitate an adjustment to the type I error. Multiplicity may arise,
for example, from multiple primary variables (see Section 2.2.2), multiple
comparisons of treatments, repeated evaluation over time and/or interim analyses
26
(see Section 4.5). Methods to avoid or reduce multiplicity are sometimes preferable
when available, such as the identification of the key primary variable (multiple
variables), the choice of a critical treatment contrast (multiple comparisons), the use
of a summary measure such as ‘area under the curve’ (repeated measures). In
confirmatory analyses, any aspects of multiplicity which remain after steps of this
kind have been taken should be identified in the protocol; adjustment should always
be considered and the details of any adjustment procedure or an explanation of why
adjustment is not thought to be necessary should be set out in the analysis plan.
5.7 Subgroups, Interactions and Covariates
The primary variable(s) is often systematically related to other influences apart from
treatment. For example, there may be relationships to covariates such as age and sex,
or there may be differences between specific subgroups of subjects such as those
treated at the different centres of a multicentre trial. In some instances an
adjustment for the influence of covariates or for subgroup effects is an integral part of
the planned analysis and hence should be set out in the protocol. Pre-trial
deliberations should identify those covariates and factors expected to have an
important influence on the primary variable(s), and should consider how to account
for these in the analysis in order to improve precision and to compensate for any lack
of balance between treatment groups. If one or more factors are used to stratify the
design, it is appropriate to account for those factors in the analysis. When the
potential value of an adjustment is in doubt, it is often advisable to nominate the
unadjusted analysis as the one for primary attention, the adjusted analysis being
supportive. Special attention should be paid to centre effects and to the role of
baseline measurements of the primary variable. It is not advisable to adjust the main
analyses for covariates measured after randomisation because they may be affected
by the treatments.
The treatment effect itself may also vary with subgroup or covariate - for example,
the effect may decrease with age or may be larger in a particular diagnostic category
of subjects. In some cases such interactions are anticipated or are of particular prior
interest (e.g. geriatrics), and hence a subgroup analysis, or a statistical model
including interactions, is part of the planned confirmatory analysis. In most cases,
however, subgroup or interaction analyses are exploratory and should be clearly
identified as such; they should explore the uniformity of any treatment effects found
overall. In general, such analyses should proceed first through the addition of
interaction terms to the statistical model in question, complemented by additional
exploratory analysis within relevant subgroups of subjects, or within strata defined
by the covariates. When exploratory, these analyses should be interpreted cautiously;
any conclusion of treatment efficacy (or lack thereof) or safety based solely on
exploratory subgroup analyses are unlikely to be accepted.
5.8 Integrity of Data and Com puter Software Validity
The credibility of the numerical results of the analysis depends on the quality and
validity of the methods and software (both internally and externally written) used
both for data management (data entry, storage, verification, correction and retrieval)
and also for processing the data statistically. Data management activities should
therefore be based on thorough and effective standard operating procedures. The
computer software used for data management and statistical analysis should be
reliable, and documentation of appropriate software testing procedures should be
available.
27
VI. EVALUATION OF SAFETY AND TOLERABILITY
6.1 Scope of Evaluation
In all clinical trials evaluation of safety and tolerability (see Glossary) constitutes an
important element. In early phases this evaluation is mostly of an exploratory nature,
and is only sensitive to frank expressions of toxicity, whereas in later phases the
establishment of the safety and tolerability profile of a drug can be characterised
more fully in larger samples of subjects. Later phase controlled trials represent an
important means of exploring in an unbiased manner any new potential adverse
effects, even if such trials generally lack power in this respect.
Certain trials may be designed with the purpose of making specific claims about
superiority or equivalence with regard to safety and tolerability compared to another
drug or to another dose of the investigational drug. Such specific claims should be
supported by relevant evidence from confirmatory trials, similar to that necessary for
corresponding efficacy claims.
6.2 Choice of Variables and Data Collection
In any clinical trial the methods and measurements chosen to evaluate the safety and
tolerability of a drug will depend on a number of factors, including knowledge of the
adverse effects of closely related drugs, information from non-clinical and earlier
clinical trials and possible consequences of the pharmacodynamic/pharmacokinetic
properties of the particular drug, the mode of administration, the type of subjects to
be studied, and the duration of the trial. Laboratory tests concerning clinical
chemistry and haematology, vital signs, and clinical adverse events (diseases, signs
and symptoms) usually form the main body of the safety and tolerability data. The
occurrence of serious adverse events and treatment discontinuations due to adverse
events are particularly important to register (see ICH E2A and ICH E3).
Furthermore, it is recommended that a consistent methodology be used for the data
collection and evaluation throughout a clinical trial program in order to facilitate the
combining of data from different trials. The use of a common adverse event dictionary
is particularly important. This dictionary has a structure which gives the possibility
to summarise the adverse event data on three different levels; system-organ class,
preferred term or included term (see Glossary). The preferred term is the level on
which adverse events usually are summarised, and preferred terms belonging to the
same system-organ class could then be brought together in the descriptive
presentation of data (see ICH M1).
6.3 Set of Subjects to be Evaluated and Presentation of Data
For the overall safety and tolerability assessment, the set of subjects to be
summarised is usually defined as those subjects who received at least one dose of the
investigational drug. Safety and tolerability variables should be collected as
comprehensively as possible from these subjects, including type of adverse event,
severity, onset and duration (see ICH E2B). Additional safety and tolerability
evaluations may be needed in specific subpopulations, such as females, the elderly
(see ICH E7), the severely ill, or those who have a common concomitant treatment.
These evaluations may need to address more specific issues (see ICH E3).
All safety and tolerability variables will need attention during evaluation, and the
broad approach should be indicated in the protocol. All adverse events should be
reported, whether or not they are considered to be related to treatment. All available
data in the study population should be accounted for in the evaluation. Definitions of
measurement units and reference ranges of laboratory variables should be made with
care; if different units or different reference ranges appear in the same trial (e.g. if
28
more than one laboratory is involved), then measurements should be appropriately
standardised to allow a unified evaluation. Use of a toxicity grading scale should be
prespecified and justified.
The incidence of a certain adverse event is usually expressed in the form of a
proportion relating number of subjects experiencing events to number of subjects at
risk. However, it is not always self-evident how to assess incidence. For example,
depending on the situation the number of exposed subjects or the extent of exposure
(in person-years) could be considered for the denominator. Whether the purpose of the
calculation is to estimate a risk or to make a comparison between treatment groups it
is important that the definition is given in the protocol. This is especially important if
long-term treatment is planned and a substantial proportion of treatment
withdrawals or deaths are expected. For such situations survival analysis methods
should be considered and cumulative adverse event rates calculated in order to avoid
the risk of underestimation.
In situations when there is a substantial background noise of signs and symptoms
(e.g. in psychiatric trials) one should consider ways of accounting for this in the
estimation of risk for different adverse events. One such method is to make use of the
'treatment emergent' (see Glossary) concept in which adverse events are recorded only
if they emerge or worsen relative to pretreatment baseline.
Other methods to reduce the effect of the background noise may also be appropriate
such as ignoring adverse events of mild severity or requiring that an event should
have been observed at repeated visits to qualify for inclusion in the numerator. Such
methods should be explained and justified in the protocol.
6.4 Statistical Evaluation
The investigation of safety and tolerability is a multidimensional problem. Although
some specific adverse effects can usually be anticipated and specifically monitored for
any drug, the range of possible adverse effects is very large, and new and
unforeseeable effects are always possible. Further, an adverse event experienced after
a protocol violation, such as use of an excluded medication, may introduce a bias. This
background underlies the statistical difficulties associated with the analytical
evaluation of safety and tolerability of drugs, and means that conclusive information
from confirmatory clinical trials is the exception rather than the rule.
In most trials the safety and tolerability implications are best addressed by applying
descriptive statistical methods to the data, supplemented by calculation of confidence
intervals wherever this aids interpretation. It is also valuable to make use of
graphical presentations in which patterns of adverse events are displayed both within
treatment groups and within subjects.
The calculation of p-values is sometimes useful either as an aid to evaluating a
specific difference of interest, or as a 'flagging' device applied to a large number of
safety and tolerability variables to highlight differences worth further attention. This
is particularly useful for laboratory data, which otherwise can be difficult to
summarise appropriately. It is recommended that laboratory data be subjected to
both a quantitative analysis, e.g. evaluation of treatment means, and a qualitative
analysis where counting of numbers above or below certain thresholds are calculated.
If hypothesis tests are used, statistical adjustments for multiplicity to quantify the
type I error are appropriate, but the type II error is usually of more concern. Care
should be taken when interpreting putative statistically significant findings when
there is no multiplicity adjustment.
29
In the majority of trials investigators are seeking to establish that there are no
clinically unacceptable differences in safety and tolerability compared with either a
comparator drug or a placebo. As is the case for non-inferiority or equivalence
evaluation of efficacy the use of confidence intervals is preferred to hypothesis testing
in this situation. In this way, the considerable imprecision often arising from low
frequencies of occurrence is clearly demonstrated.
6.5 Integrated Summary
The safety and tolerability properties of a drug are commonly summarised across
trials continuously during an investigational product’s development and in particular
at the time of a marketing application. The usefulness of this summary, however, is
dependent on adequate and well-controlled individual trials with high data quality.
The overall usefulness of a drug is always a question of balance between risk and
benefit and in a single trial such a perspective could also be considered, even if the
assessment of risk/benefit usually is performed in the summary of the entire clinical
trial program. (See section 7.2.2)
For more details on the reporting of safety and tolerability, see Chapter 12 of ICH E3.
VII.
REPORTING
7.1 Evaluation and Reporting
As stated in the Introduction, the structure and content of clinical study reports is the
subject of ICH E3. That ICH guidance fully covers the reporting of statistical work,
appropriately integrated with clinical and other material. The current section is
therefore relatively brief.
During the planning phase of a trial the principal features of the analysis should have
been specified in the protocol as described in Section 5. When the conduct of the trial
is over and the data are assembled and available for preliminary inspection, it is
valuable to carry out the blind review of the planned analysis also described in
Section 5. This pre-analysis review, blinded to treatment, should cover decisions
concerning, for example, the exclusion of subjects or data from the analysis sets;
possible transformations may also be checked, and outliers defined; important
covariates identified in other recent research may be added to the model; the use of
parametric or non-parametric methods may be reconsidered. Decisions made at this
time should be described in the report, and should be distinguished from those made
after the statistician has had access to the treatment codes, as blind decisions will
generally introduce less potential for bias. Statisticians or other staff involved in
unblinded interim analysis should not participate in the blind review or in making
modifications to the statistical analysis plan. When the blinding is compromised by
the possibility that treatment induced effects may be apparent in the data, special
care will be needed for the blind review.
Many of the more detailed aspects of presentation and tabulation should be finalised
at or about the time of the blind review so that by the time of the actual analysis full
plans exist for all its aspects including subject selection, data selection and
modification, data summary and tabulation, estimation and hypothesis testing. Once
data validation is complete, the analysis should proceed according to the pre-defined
plans; the more these plans are adhered to, the greater the credibility of the results.
Particular attention should be paid to any differences between the planned analysis
and the actual analysis as described in the protocol, protocol amendments or the
updated statistical analysis plan based on a blind review of data. A careful
explanation should be provided for deviations from the planned analysis.
30
All subjects who entered the trial should be accounted for in the report, whether or
not they are included in the analysis. All reasons for exclusion from analysis should
be documented; for any subject included in the full analysis set but not in the per
protocol set, the reasons for exclusion from the latter should also be documented.
Similarly, for all subjects included in an analysis set, the measurements of all
important variables should be accounted for at all relevant time-points.
The effect of all losses of subjects or data, withdrawals from treatment and major
protocol violations on the main analyses of the primary variable(s) should be
considered carefully. Subjects lost to follow up, withdrawn from treatment, or with a
severe protocol violation should be identified, and a descriptive analysis of them
provided, including the reasons for their loss and its relationship to treatment and
outcome.
Descriptive statistics form an indispensable part of reports. Suitable tables and/or
graphical presentations should illustrate clearly the important features of the
primary and secondary variables and of key prognostic and demographic variables.
The results of the main analyses relating to the objectives of the trial should be the
subject of particularly careful descriptive presentation. When reporting the results of
significance tests, precise p-values (e.g.'p=0.034') should be reported rather than
making exclusive reference to critical values.
Although the primary goal of the analysis of a clinical trial should be to answer the
questions posed by its main objectives, new questions based on the observed data may
well emerge during the unblinded analysis. Additional and perhaps complex
statistical analysis may be the consequence. This additional work should be strictly
distinguished in the report from work which was planned in the protocol.
The play of chance may lead to unforeseen imbalances between the treatment groups
in terms of baseline measurements not pre-defined as covariates in the planned
analysis but having some prognostic importance nevertheless. This is best dealt with
by showing that an additional analysis which accounts for these imbalances reaches
essentially the same conclusions as the planned analysis. If this is not the case, the
effect of the imbalances on the conclusions should be discussed.
In general, sparing use should be made of unplanned analyses. Such analyses are
often carried out when it is thought that the treatment effect may vary according to
some other factor or factors. An attempt may then be made to identify subgroups of
subjects for whom the effect is particularly beneficial. The potential dangers of overinterpretation of unplanned subgroup analyses are well known (see also Section 5.7),
and should be carefully avoided. Although similar problems of interpretation arise if a
treatment appears to have no benefit, or an adverse effect, in a subgroup of subjects,
such possibilities should be properly assessed and should therefore be reported.
Finally statistical judgement should be brought to bear on the analysis, interpretation
and presentation of the results of a clinical trial. To this end the trial statistician
should be a member of the team responsible for the clinical study report, and should
approve the clinical report.
7.2 Summarising the Clinical Database
An overall summary and synthesis of the evidence on safety and efficacy from all the
reported clinical trials is required for a marketing application (Expert report in EU,
integrated summary reports in USA, Gaiyo in Japan). This may be accompanied,
when appropriate, by a statistical combination of results.
Within the summary a number of areas of specific statistical interest arise: describing
the demography and clinical features of the population treated during the course of
31
the clinical trial programme; addressing the key questions of efficacy by considering
the results of the relevant (usually controlled) trials and highlighting the degree to
which they reinforce or contradict each other; summarising the safety information
available from the combined database of all the trials whose results contribute to the
marketing application and identifying potential safety issues. During the design of a
clinical programme careful attention should be paid to the uniform definition and
collection of measurements which will facilitate subsequent interpretation of the
series of trials, particularly if they are likely to be combined across trials. A common
dictionary for recording the details of medication, medical history and adverse events
should be selected and used. A common definition of the primary and secondary
variables is nearly always worthwhile, and essential for meta-analysis. The manner
of measuring key efficacy variables, the timing of assessments relative to
randomisation/entry, the handling of protocol violators and deviators and perhaps the
definition of prognostic factors, should all be kept compatible unless there are valid
reasons not to do so.
Any statistical procedures used to combine data across trials should be described in
detail. Attention should be paid to the possibility of bias associated with the selection
of trials, to the homogeneity of their results, and to the proper modelling of the
various sources of variation. The sensitivity of conclusions to the assumptions and
selections made should be explored.
7.2.1 Efficacy Data
Individual clinical trials should always be large enough to satisfy their objectives.
Additional valuable information may also be gained by summarising a series of
clinical trials which address essentially identical key efficacy questions. The main
results of such a set of trials should be presented in an identical form to permit
comparison, usually in tables or graphs which focus on estimates plus confidence
limits. The use of meta-analytic techniques to combine these estimates is often a
useful addition, because it allows a more precise overall estimate of the size of the
treatment effects to be generated, and provides a complete and concise summary of
the results of the trials. Under exceptional circumstances a meta analytic approach
may also be the most appropriate way, or the only way, of providing sufficient overall
evidence of efficacy via an overall hypothesis test. When used for this purpose the
meta-analysis should have its own prospectively written protocol.
7.2.2 Safety Data
In summarising safety data it is important to examine the safety database thoroughly
for any indications of potential toxicity, and to follow up any indications by looking for
an associated supportive pattern of observations. The combination of the safety data
from all human exposure to the drug provides an important source of information,
because its larger sample size provides the best chance of detecting the rarer adverse
events and, perhaps, of estimating their approximate incidence. However, incidence
data from this database are difficult to evaluate because of the lack of a comparator
group, and data from comparative trials are especially valuable in overcoming this
difficulty. The results from trials which use a common comparator (placebo or specific
active comparator) should be combined and presented separately for each comparator
providing sufficient data.
All indications of potential toxicity arising from exploration of the data should be
reported. The evaluation of the reality of these potential adverse effects should take
account of the issue of multiplicity arising from the numerous comparisons made. The
evaluation should also make appropriate use of survival analysis methods to exploit
the potential relationship of the incidence of adverse events to duration of exposure
32
and/or follow-up. The risks associated with identified adverse effects should be
appropriately quantified to allow a proper assessment of the risk/benefit relationship.
GLOSSARY
Bayesian Approaches
Approaches to data analysis that provide a posterior probability distribution for some
parameter (e.g. treatment effect), derived from the observed data and a prior
probability distribution for the parameter. The posterior distribution is then used as
the basis for statistical inference.
Bias (Statistical & Operational)
The systematic tendency of any factors associated with the design, conduct, analysis
and evaluation of the results of a clinical trial to make the estimate of a treatment
effect deviate from its true value. Bias introduced through deviations in conduct is
referred to as 'operational' bias. The other sources of bias listed above are referred to
as 'statistical'.
Blind Review
The checking and assessment of data during the period of time between trial
completion (the last observation on the last subject) and the breaking of the blind, for
the purpose of finalising the planned analysis.
Content Validity
The extent to which a variable (e.g. a rating scale) measures what it is supposed to
measure.
Double-Dummy
A technique for retaining the blind when administering supplies in a clinical trial,
when the two treatments cannot be made identical. Supplies are prepared for
Treatment A (active and indistinguishable placebo) and for Treatment B (active and
indistinguishable placebo). Subjects then take two sets of treatment; either A (active)
and B (placebo), or A (placebo) and B (active).
Dropout
A subject in a clinical trial who for any reason fails to continue in the trial until the
last visit required of him/her by the study protocol.
Equivalence Trial
A trial with the primary objective of showing that the response to two or more
treatments differs by an amount which is clinically unimportant. This is usually
demonstrated by showing that the true treatment difference is likely to lie between a
lower and an upper equivalence margin of clinically acceptable differences.
Frequentist Methods
Statistical methods, such as significance tests and confidence intervals, which can be
interpreted in terms of the frequency of certain outcomes occurring in hypothetical
repeated realisations of the same experimental situation.
Full Analysis Set
33
The set of subjects that is as close as possible to the ideal implied by the intention-totreat principle. It is derived from the set of all randomised subjects by minimal and
justified elimination of subjects.
Generalisability, Generalisation
The extent to which the findings of a clinical trial can be reliably extrapolated from
the subjects who participated in the trial to a broader patient population and a
broader range of clinical settings.
Global Assessment Variable
A single variable, usually a scale of ordered categorical ratings, which integrates
objective variables and the investigator's overall impression about the state or change
in state of a subject.
Independent Data Monitoring Committee (IDMC) (Data and Safety
Monitoring Board, Monitoring Committee, Data Monitoring Committee)
An independent data-monitoring committee that may be established by the sponsor to
assess at intervals the progress of a clinical trial, the safety data, and the critical
efficacy endpoints, and to recommend to the sponsor whether to continue, modify, or
stop a trial.
Intention-To-Treat Principle
The principle that asserts that the effect of a treatment policy can be best assessed by
evaluating on the basis of the intention to treat a subject (i.e. the planned treatment
regimen) rather than the actual treatment given. It has the consequence that subjects
allocated to a treatment group should be followed up, assessed and analysed as
members of that group irrespective of their compliance to the planned course of
treatment.
Interaction (Qualitative & Quantitative)
The situation in which a treatment contrast (e.g. difference between investigational
product and control) is dependent on another factor (e.g. centre). A quantitative
interaction refers to the case where the magnitude of the contrast differs at the
different levels of the factor, whereas for a qualitative interaction the direction of the
contrast differs for at least one level of the factor.
Inter-Rater Reliability
The property of yielding equivalent results when used by different raters on different
occasions.
Intra-Rater Reliability
The property of yielding equivalent results when used by the same rater on different
occasions.
Interim Analysis
Any analysis intended to compare treatment arms with respect to efficacy or safety at
any time prior to the formal completion of a trial.
Meta-Analysis
The formal evaluation of the quantitative evidence from two or more trials bearing on
the same question. This most commonly involves the statistical combination of
34
summary statistics from the various trials, but the term is sometimes also used to
refer to the combination of the raw data.
Multicentre Trial
A clinical trial conducted according to a single protocol but at more than one site, and
therefore, carried out by more than one investigator.
Non-Inferiority Trial
A trial with the primary objective of showing that the response to the investigational
product is not clinically inferior to a comparative agent (active or placebo control).
Preferred and Included Terms
In a hierarchical medical dictionary, for example MedDRA, the included term is the
lowest level of dictionary term to which the investigator description is coded. The
preferred term is the level of grouping of included terms typically used in reporting
frequency of occurrence. For example, the investigator text “Pain in the left arm”
might be coded to the included term “Joint pain”, which is reported at the preferred
term level as “Arthralgia”.
Per Protocol Set (Valid Cases, Efficacy Sample, Evaluable Subjects Sample)
The set of data generated by the subset of subjects who complied with the protocol
sufficiently to ensure that these data would be likely to exhibit the effects of
treatment, according to the underlying scientific model. Compliance covers such
considerations as exposure to treatment, availability of measurements and absence of
major protocol violations.
Safety & Tolerability
The safety of a medical product concerns the medical risk to the subject, usually
assessed in a clinical trial by laboratory tests (including clinical chemistry and
haematology), vital signs, clinical adverse events (diseases, signs and symptoms), and
other special safety tests (e.g. ECGs, ophthalmology). The tolerability of the medical
product represents the degree to which overt adverse effects can be tolerated by the
subject.
Statistical Analysis Plan
A statistical analysis plan is a document that contains a more technical and detailed
elaboration of the principal features of the analysis described in the protocol, and
includes detailed procedures for executing the statistical analysis of the primary and
secondary variables and other data.
Superiority Trial
A trial with the primary objective of showing that the response to the investigational
product is superior to a comparative agent (active or placebo control).
Surrogate Variable
A variable that provides an indirect measurement of effect in situations where direct
measurement of clinical effect is not feasible or practical.
Treatment Effect
An effect attributed to a treatment in a clinical trial. In most clinical trials the
treatment effect of interest is a comparison (or contrast) of two or more treatments.
35
Treatment Emergent
An event that emerges during treatment having been absent pre-treatment, or
worsens relative to the pre-treatment state.
Trial Statistician
A statistician who has a combination of education/training and experience sufficient
to implement the principles in this guidance and who is responsible for the statistical
aspects of the trial.
36
Ruolo di
differenti insiemi di pazienti
nell’analisi delle
dr.ssa Bacchieri
“Ruolo di differenti insiemi di pazienti
nell’analisi delle sperimentazioni cliniche”
Antonella Bacchieri
Le competenze biostatistiche nell’ambito dei
Comitati Etici locali
(Roma, 18 marzo 2003)
Differenti insiemi di pazienti (Popolazioni)
•
•
•
•
Safety
Full Set ~ Intention-To-Treat (ITT)
Completers ( <=> diverse definizioni)
Completers/Compliers~Per Protocol~Efficacy
Sample~Valid Cases~Evaluable Subjects ( <=> diverse
definizioni)
• Sotto-gruppi definiti sulla base di uno o più fattori
prognostici di interesse
Safety~”Full Set”
• Definizione: Tutti i pazienti randomizzati,
che hanno preso almeno una dose del
trattamento in studio
• Ruolo: risponde alla domanda:
– qual è la sicurezza del trattamento (assunto in
qualunque condizione e per qualunque
ragione)?
Safety~”Full Set”: debolezze
• I pazienti drop-out (cioè che interrompono
prematuramente lo studio) non hanno una
valutazione finale (per esempio per dati di
laboratorio e segni vitali)
ITT~ ”Full Set”
• Definizione rigorosa: Tutti i pazienti
randomizzati
• Definizione modificata: Tutti i pazienti
randomizzati, che hanno preso almeno una dose
del trattamento in studio, che hanno almeno una
valutazione post-basale (e che soddisfano i criteri
di inclusione fondamentali).
• Ruolo: risponde alla domanda:
– qual è l’effetto del trattamento in condizioni
simili a quelle reali?
ITT: obiettivi
• Fornire una stima dell’effetto del trattamento che
sia più vicina possibile alla situazione clinica reale
• Lasciare invariate le condizioni create dalla
randomizazzione, cioè non introdurre bias di
selezione (relativi a fattori prognostici noti o
ignoti) e bias di osservazione/valutazione
• Permettere di verificare se il pattern dei drop-out è
indipendente dal trattamento (verifica indiretta
tramite confronto risultati verso Completers)
ITT: debolezze
• Non escludere alcun paziente dall’analisi
generalmente crea un bias
• Tuttavia, la direzione di questo bias è nota
se sono impiegati metodi appropriati per
sostituire i valori mancanti
ITT: debolezze
• Difficoltà di scelta del metodo per sostituire le
osservazioni mancanti
– Retrieved drop-out: valutare a fine studio anche i drop-out [ Knapp
MJ et al. JAMA 13; 985-991 (1994)]
– Last-Observation-Carried-Forward (LOCF): ciascun paziente è
inserito nell’analisi con la sua ultima valutazione
– Inputation strategy:
• assegnare ai drop-out dei valori ricavati da modelli statistici, tesi a
stimare i valori che i pazienti con valori mancanti avrebbero avuto se
non fossero usciti prematuramente dallo studio (bibliografia allegata)
• assegnare ai drop-out valori pre-definiti a seconda della loro ragione
di uscita dallo studio [Gould AL. Biometrics 36; 721-727 (1980)]
• assegnare ai drop-out il valore peggiore (eventualmente assegnare il
valore migliore a quelli del gruppo di controllo)
• Metodo di analisi statistica (la scelta deve tenere in
considerazione la scelta sopra descritta)
ITT: Scelta del metodo
• BIAS
• INTERPRETABILITA’
• PROBABILITA’ DELL’ERRORE DI
PRIMO TIPO
• POTENZA
Completers (1)
• “Completers” relativamente alle visite di
valutazione dello studio
– Definizione: Tutti i pazienti che hanno una
valutazione alla visita finale (o ad un certo
numero pre-definito di visite) almeno per l’endpoint primario
– Ruolo: “risponde” alla domanda:
• qual è l’effetto del trattamento senza influenza delle
distorsioni generate dal metodo di sostituzione delle
osservazioni mancanti?
Completers (2)
• “Completers” relativamente all’esposizione
al trattamento in studio
– Definizione: Tutti i pazienti che hanno avuto
un’esposizione completa (o almeno di una certa
entità) al trattamento
– Ruolo: “risponde” alla domanda:
• qual è l’effetto del trattamento nel sotto-insieme dei
pazienti che possono/vogliono prendere tutto il
trattamento, come definito dal protocollo?
Completers: obiettivi
• Ottenere stime del trattamento più
facilmente interpretabili da un punto di vista
clinico rispetto a quelle ottenute sulla
popolazione ITT
Completers: debolezze
• BIAS: i pazienti che completano lo studio
possono non essere rappresentativi di tutti i
pazienti per ragioni legate al trattamento
• Generalmente, interpretabile solo in
presenza di un risultato significativo a
livello di ITT
Completers/Compliers ~Per Protocol ~Efficacy
Sample~Valid Cases~Evaluable Subjects (1)
• Definizione: Sotto-insieme della popolazione
Completers (1) che ha rispettato il protocollo
(criteri di eleggibilità, “buona” compliance al
trattamento sperimentale, rispetto delle procedure
fissate dal protocollo)
• Ruolo: “risponde” alla domanda:
– qual è l’effetto del trattamento nelle migliori
condizioni sperimentali (popolazione target che
riesce a completare lo studio come pianificato)?
Completers/Compliers ~Per Protocol ~Efficacy
Sample~Valid Cases~Evaluable Subjects (2)
• Definizione: Sotto-insieme della popolazione ITT
che ha rispettato il protocollo (criteri di
eleggibilità, “buona” compliance al trattamento
sperimentale, rispetto delle procedure fissate dal
protocollo)
• Ruolo: “risponde” alla domanda:
– qual è l’effetto del trattamento nella
popolazione target?
Completers/Compliers...: obiettivi
• Massimizzare le opportunità del trattamento
in studio di dimostrare un effetto
• Fornire una stima dell’effetto del
trattamento che corrisponde al modello
scientifico sottostante il protocollo
Completers/Compliers...: debolezze
• BIAS (potenzialmente rilevante): l’aderenza
al protocollo può essere correlata con il
trattamento e con il risultato dello studio
• Pertanto, interpretabile solo in presenza di
un risultato significativo a livello di ITT
Sottogruppi definiti sulla base di uno o più
fattori prognostici di interesse
• Definizione: Sotto-gruppi della popolazione ITT
definiti rispetto ai livelli di un fattore prognostico
di interesse (es. giovani /anziani, maschi/femmine,
ecc…)
• Ruolo: le analisi di sotto-gruppo “rispondono”
alla domanda:
– l’effetto del trattamento varia al variare del
fattore prognostico di interesse? (~ C’è
interazione trattamento-fattore prognostico?)
Sottogruppi definiti sulla base di fattori
prognostici: obiettivi
• Bisogna distinguere il caso in cui gli strati
sono pre-definiti (~definiti da una
randomizzazione stratificata) dal caso in cui
la stratificazione è introdotta a posteriori
• In ogni caso: l’obiettivo principale è quello
di generare ipotesi
Sottogruppi definiti sulla base di fattori
prognostici: debolezze
• Molteplicità dei test statistici
• Mancanza di potenza dei test statistici per
verificare l’interazione
• Risultati interpretabili solo in presenza di un
risultato significativo a livello globale (la presenza
d’interazione quando non c’è effetto del
trattamento a livello complessivo dello studio
implica interazione qualitativa)
• Se la stratificazione è introdotta a posteriori: BIAS
Principali Popolazioni di Analisi
Studi di superiorità
(generalmente end-point di
efficacia)
• Analisi primaria:
– ITT
• Analisi secondaria:
– Completers (1)
– Completers/Compliers
(1) o (2)
Studi di equivalenza (o non
inferiorità)
– Analisi di efficacia:
– ITT
– Completers/Compliers (1) o (2)
– Completers (1)
– Analisi di sicurezza e tollerabilità:
– Safety
– Completers/Compliers (1)
Punto di vista dell’autorità regolatoria
• ICH Harmonized tripartite Guideline E9.
Statistical Principles for Clinical Trials.
• [CPMP Working party on Efficacy of Medicinal
Products. Note for guidance III/3630/92-EN:
Biostatistical Methodology in clinical trials in
applications for marketing authorizations for
medicinal products. Statistics in Medicine 14;
1659-1682 (1995)]
Punto di vista dell’autorità regolatoria
(da ICH E9)
• In confirmatory (and superiority) trials it is usually appropriate to plan
to conduct both an analysis of the full set and a per protocol analysis
(main sets)
• The set of subjects whose data are to be included in the main analysis
should be defined in the protocol and decisions should be guided by
the following principles: minimize bias and avoid inflation of type I
error (=The full set analysis is primary). Robustness of results to
methods for evaluating withdrawals must be investigated.
• Any differences between results obtained in the two analysis sets can
be subject of explicit discussion and interpretation (sensitivity analysis)
• Tutte le scelte “should be described and justified in the statistical
section of the protocol” mentre considerazioni più dettagliate “can be
deferred until the blind review of the data”
• The full analysis set and the per protocol sets play different roles in
superiority trials and in equivalence or non-inferiority trials
Bibliografia su linee guida
• S. Lange (2001). “The all randomized/full analysis
set (ICH E9) - May patients be excluded from the
analysis?” DIJ, 35; 881-891
• D. Gillings & G. Kock (1991). “The application of
the principle of intention-to-treat to the analysis of
clinical trials”. DIJ, 25; 441-424
Modified ITT (da ICH E9)
“There are a limited number of circumstances that
might lead to excluding randomized subjects from
the full analysis, including:
– the failure to satisfy major entry criteria;
– the failure to take at least one dose of trial
medication;
– the lack of any data post randomization”
Modified ITT: failure to satisfy major
entry criteria (da ICH E9)
“Subjects who fail to satisfy an entry criterion may be
excluded from the analysis without the possibility of
introducing bias only under the following circumstances:
– the entry criterion was measured prior to
randomization;
– the detection of the relevant eligibility violations can be
made completely objectively;
– all subjects receive equal scrutiny for eligibility
violations;
– all detected violations of the particular entry criterion
are excluded”
Problemi/dubbi
• Cosa significa “major”? Se la % di pazienti che non
soddisfano i criteri “major” di inclusione è bassa, la loro
inclusione/esclusione è ininfluente; se tale % è alta:
– lo studio può avere problemi di qualità
– la definizione di “major” è troppo ampia.
• Caso particolare: studi clinici con agenti anti-infettivi
• Cosa significa “objective detection”?
• Se la selezione dei pazienti da escludere dall’analisi non
poteva essere fatta al momento della randomizzazione, ciò
significa che anche il medico che tratterà i pazienti sarà
nelle stesse condizioni
“Inclusion criteria are to help the physician
decide who should enter the trial, not to tell
the statistician who should enter the
analysis”
Stephen Senn
Modified ITT: failure to take at least one
dose of trial medication
La linea guida E9 non dedica alcuna
riflessione a questo aspetto, salvo dire
“provided that the decision of whether or
not to begin treatment could not be
influenced by knowledge of the assigned
treatment”.
Problemi/dubbi
• Come ci si comparta negli studi “open
label” o “single blind”?
• Non si può essere certi che il fatto che il
trattamento non sia stato neppure iniziato
non sia connesso con la conoscenza del
trattamento => ciò ha un impatto sulla
comparabilità tra i trattamenti
Modified ITT: lack of any data post
randomization
La linea guida E9 riporta solo questa
considerazione:
“No analysis is complete unless the
potential biases arising from their specific
exclusions, or any others, are addressed”
Problemi/dubbi
• Solo ragioni tecniche sono sicuramente non
correlate con il trattamento
Modified ITT: poor study conduct in one
center
Questa ragione d’esclusione non è
menzionata nella linea guida ICH E9
Problemi/dubbi
Eppure, se il giudizio circa la qualità è dato
in condizioni di cecità, questa ragione
d’esclusione sembrerebbe lecita, visto che:
– non ha impatto sulla randomizzazione e quindi
sulla confrontabilità dei gruppi
– non ha impatto sulla domanda clinica
sottostante, in quanto l’obiettivo del trial clinico
non è comunque quello di riflettere una scarsa
qualità.
Bibliografia su “Imputation Strategy” per
analisi ITT
• Little RJA. Journal of the Royal Statistical Society, Ser B,
41; 76-87 (1979)
• Laird NM. Statistics in Medicine 7; 305-315 (1988)
• Rubin D & Schenker N. Statistics in Medicine 10; 585-598
(1991)
• Lavori P et al. Statistics in Medicine 14; 1913-1925 (1995)
• Rubin D. Journal of the American Statistical Association
91; 473-489 (1996)
Organizzazione
delle attività formative
prof.ssa Marinoni
dr. Raschetti
Le Competenze biostatistiche nell’ambito dei Comitati Etici Locali
(Roma, 18 Marzo 2003)
Gruppo di lavoro su:
“Organizzazione delle attività formative”
Prof.ssa A. Marinoni - Dr. Raschetti
Al fine di organizzare attività formative per i biostatistici dei Comitati Etici, è necessario definire
quali siano i bisogni formativi, e quindi le abilità e competenze necessarie ad un biostatistico per
una corretta analisi di un protocollo di ricerca sottoposto al C.E.
La metodologia proposta è quella dei gruppi di lavoro (mass. 5 persone per gruppo).
Ad ogni gruppo sarà sottoposto un protocollo di ricerca presentato ad un C.E. approvato e non più
confidenziale.
Il gruppo esaminerà il protocollo secondo una delle griglie approntate, (una per ricerca
sperimentale e una per ricerca osservazionale).
Dalla discussione sull’analisi condotta emergeranno le carenze formative e le competenze
richieste. Sarà quindi approntata una proposta di corso/corsi di formazione specifica
Griglia per l’analisi critica del protocollo di un trial clinico
1) Background (razionale)
(è motivata la necessità di fare la ricerca proposta allo stato attuale delle ricerche?)
2) Obiettivo/i
-
generale/i
specifico/i
(sono definiti in termini operativi l’obiettivo principale e quelli secondari? etc.)
METODOLOGIA
3 Popolazione e Campione
(base di campionamento, criteri di inclusione-esclusione, classificazione diagnostica)
4) Disegno dello studio
-
tipo di trial (efficacia-equivalenza, etc.)
fase del trial
tipo di controllo
5) Variabili raccolte e loro ruolo
-
end-point
fattori prognostici
fattori confondenti
trattamento/i schemi di trattamento
6) Dimensioni dello studio
(criteri per la definizione del n° di pazienti per braccio, etc.)
7) Metodi di analisi statistica
(sono specificati i metodi di analisi? sono idonei, etc.)
8) Aspetti organizzativi
(è garantito un idoneo coordinamento-controllo di qualità, regole di sospensione, etc.)
9) Aspetti economici
(l’azienda in cui si fa la sperimentazione garantisce che il budget copra i costi?)
10) Foglio informativo per il paziente
(è fornita una chiara e completa presentazione degli obiettivi della ricerca, dei rischi, etc.?)
11) Aspetti assicurativi
(il medico legale e/o l’avvocato del C.E. ritengono l’assicurazione idonea per i pazienti e per i
medici sperimentatori. Non solo per i dipendenti dello sponsor)
12) Aspetti etici
-
rilevanza del problema
necessità della ricerca
correttezza della metodologia
eticità del gruppo di controllo (placebo o altro trattamento)
accettabilità dei rischi (costo-beneficio)
informazione corretta
copertura dei costi, etc.
Griglia per l’analisi critica del protocollo di uno studio osservazionale
(è motivata la necessità di fare la ricerca proposta allo stato attuale delle ricerche?)
2) Obiettivo/i
-
generale/i
specifico/i
(sono definiti in termini operativi l’obiettivo principale e quelli secondari? etc.)
METODOLOGIA
(base di campionamento, criteri di inclusione-esclusione, classificazione diagnostica)
-
tipo di trial (efficacia-equivalenza, etc.)
fase del trial
tipo di controllo
-
end-point
fattori prognostici
fattori confondenti
trattamento/i schemi di trattamento
(criteri per la definizione del n° di pazienti per braccio, etc.)
(sono specificati i metodi di analisi? sono idonei, etc.)
(è garantito un idoneo coordinamento-controllo di qualità, regole di sospensione, etc.)
(l’azienda in cui si fa la sperimentazione garantisce che il budget copra i costi?)
(è fornita una chiara e completa presentazione degli obiettivi della ricerca, dei rischi, etc.?)
11) Aspetti assicurativi (se il caso)
Griglia per la identificazione dei bisogni formativi
(studi sperimentali)
Competenze richieste
2) Obiettivo/i
11) Aspetti assicurativi
12) Aspetti etici
Griglia per la identificazione dei bisogni formativi
(studi osservazionali)
Competenze richieste
2) Obiettivo/i
10) Aspetti etici
11) Aspetti assicurativi (se il caso)
dr.ssa Menniti-Ippolito
dr.ssa Bacchieri
REVISIONE DELLE LINEE GUIDA
Le competenze biostatistiche
nell’ambito dei Comitati Etici locali
(Roma, 18 marzo 2003)
Useful addresses
•
•
•
www.ich.org
www.emea.eu.int
www.fda.gov
The European Agency for the Evaluation of Medicinal Products
Pre-authorisation evaluation of medicines for human use
EMEA
London, 18 December 2002
CPMP/EWP/478/96 — Rev. 28
CPMP Efficacy Working Party Work programme 2003-2004
I.
Meetings scheduled for 2003-2004
-
II.
Dates Plenary meeting/Number of day per meeting/number of member per meeting:
•
For 2003: 27-28 January; 8-9 April; 8-9 July and 7-8 October
•
For 2004: four two-days meeting (dates to be defined)
Product related issues (such as support to Marketing Authorisation Assessment, Post-marketing Data
Evaluation, Scientific Advice, Protocol Assistance).
The following table provides, the expected number per year of contribution (number of involment in
dossier) for Scientific Advice, Protocol Assistance, Product Assessment and Post-Authorisation issue
(pharmacovigilance issue related to a product or a class of product).
Expected
contribution in
Scientific Advice
Efficacy Working
Party contribution
per year.
10
Expected
contribution in
Protocol
Assistance
2
Expected
contribution in
Product
Assessment
2
Expected contribution in
Post-authorisation issue
2
III.
CPMP Guidance documents
1.
CNS/Pain:
•
Addendum on neuropathic pain to the Note for Guidance on clinical investigation of medicinal products
for nociceptive Pain treatment (CPMP/EWP/612/00).
Action: Concept paper expected in 1/2Q2003.
•
Note for Guidance on the evaluation of medicinal products for treatment of migraine
(CPMP/EWP/788/01).
Action: Released for 6-month consultation in September 2002 for comments by March 2003, expected
to be finalised 3/4Q2003.
•
Appendix to the CPMP Note for guidance on the clinical investigation of medicinal products in the
treatment of schizophrenia (CPMP/EWP/559/95), on methodology of clinical trials concerning the
development of depot preparations of approved medicinal products in schizophrenia
(CPMP/EWP/49/01).
Action: Released for 6-month consultation in February 2002 for comments by August 2002 expected to
be finalised 1/2Q2003.
•
Note for Guidance on Clinical Investigation of Medical Products in the Treatment of Generalised Anxiety
Disorder.
Action: Concept Paper on the revision of the existing guideline adopted in November 2002.
Release for consultation expected in 3Q2003.
7 Westferry Circus, Canary Wharf, London, E14 4HB, UK
Tel. (44-20) 74 18 84 00 Fax (44-20) 74 18 86 70
E-mail: [email protected] www.emea.eu.int
©EMEA 2002 Reproduction and/or distribution of this document is authorised for non commercial purposes only provided the EMEA is acknowledged
•
Note for Guidance on Clinical Investigation of Medical Products in the Treatment of Panic Disorder.
•
Note for Guidance on Clinical Investigation of Medical Products in the Treatment of Obsessivecompulsive Disorder.
2.
Cardio-vascular:
•
Note for Guidance on the clinical development of Fibrinolytic medicinal products in the treatment of
patients with ST segment elevation acute myocardial infarction (STEMI) (CPMP/EWP/967/01). Rap
ES/DE
Action: Released for 3-month consultation in November 2002 for comments by February 2003, expected
to be finalised 3/4Q2003.
•
Addendum on acute cardiac failure to the CPMP Note for Guidance on clinical investigation of medicinal
products in the treatment of acute cardiac failure (CPMP/EWP/1533/01).
Action: Release for consultation expected 1/2Q2003
•
Note for guidance on the evaiuation of medicinal products for the treatment of dyslipoproteinaemia
(CPMP/EWP/5 12/01)
Action: Release for consultation expected 1/2Q2003.
•
Note for guidance on the clinical investigation of antianginal medicinal products in stable angina
pectoris. (CPMP/EWP/234/95).
Action: Revision to be considered.
3.
Rhumatology/Endocrinology:
•
Points to Consider on clinical investigation of medicinal products for treatment of Rheumatoid
arthritis (CPMP/EWP/556/95-Rev. 1), Rapp-DE/SV/BE
Action: Released for 3-month consultation in July 2002 for comments by October 2002.
Finalisation expected 2Q2003.
•
Nfg on Clinical investigation of steroid contraceptives in women (CPMP/EWP/519/98).
Action: Draft revision expected to be released for consultation 1Q2003
4.
Gastro-intestinal:
•
Points to consider on irritable bowel syndrome (IBS) (CPMP/EWP/785/97)
Action: Released for 3-month consultation in April 2002 for comments by July 2002. Finalisation
expected 1/2Q2003.
5.
Antibacterials/Anti-infectives/Vaccines:
•
Ptc document on the evaluation of new anti-fungal agents for invasive fungal infections
(CPMP/EWP/1343/01).
Action: Released for 6-month consultation in July 2002 for comments by January 2003.
Finalisation expected 2/3Q2003
•
Revision of NfG on evaluation of new anti-bacterial medicinal products (CPMP/EWP/558/95) and NfG
on the pharmacodynamic section of the summary of product characteristics for antibacterial medicinal
products (CPMP/EWP/96)
•
Note for Guidance on the clinical development of medicinal products for the treatment of HIV infection
(CPMP/EWP/633/02).
Action: Released for consultation in July 2002 for comments by October 2002. Finalisation expected
1/2Q2003
•
Thiomersal: Muldisciplinary Ptc for the omission/removal of thiomersal from vaccines
Multidisciplinary Guideline: Other involved WP: BWP, SWP, PhvWP
6.
Biostatistical/methodological issues:
٧
•
Points to consider on Biostatistical/methodological issues arising from CPMP discussion on licensing
applications: Choice of Non-inferiority margin (CPMP/EWP/2158/99)
Action: Release for consultation expected in 2003
٧
•
Points to consider on Biostatistical/methodological issues arising from CPMP discussion on licensing
applications: Adjustment for baseline covariates (CPMP/EWP/2863/99)
Action: Released for 3-month consultation in December 2001. Finalisation expected 1Q2003.
٧
•
Points to consider on the use of statistical methods for flexible design and analysis of confirmatory
clinical trials (CPMP/EWP/2459/02).
Action: Released for 3-month consultation expected 1Q2003.
٧
•
Points to consider on the use of statistical methods for flexible design and analysis of confirmatory
clinical trials (CPMP/EWP/2459/02).
Action: Released for 3-month consultation expected 1Q2003.
7.
Pharmacokinetlc topics
•
Points to consider on clinical pharmacokinetic investigation of the pharmacokinetics of peptides and
proteins (CPMP/EWP/226/02).
Action: Release for consultation expected 2Q2003.
•
Note for guidance on the evaluation of the pharmacokinetics of medicinal products in patients with
impaired renal function (CPMP/EWP/225/02).
•
Points to consider on the evaluation of the pharmacokinetics of medicinal products in the paediatric
population (CPMP/EWP/968/02).
•
Note for guidance on the evaluation of the pharmacokinetics of medicinal products in patients with
hepatic impairment (CPMP/EWP/2339/02).
8.
Others:
•
Points to consider on the requirements for clinical documentation for Metered dose inhalers
(CPMP/EWP/4151/00)
Action: Released for 3-month consultation in January 2002 for comments by April 2002.
Finalisation expected by 1/2Q2003.
•
Note for guidance on clinical investigation of medicinal products for the treatment of psoriasis
(CPMP/EWP/2454/02).
Action: Release for consultation expected in 2Q2003.
•
Points to consider on allergic rhino-conjuctivitis (CPMP/EWP/2455/02).
Action: Release for 6-month consultation expected in 1/2Q2003.
•
Addendum to the Note For Guidance on Modified Release Oral and Transdermal Dosage Forms:
Section 11 (PharmacoKinetic and Clinical Evaluation) (CPMP/EWP/280/96) on the clinical requirements
of modified release products submitted as a line-extension of an existing marketing authorisation.
•
Points to consider on Xenogenic Cell Therapy (CPMP/3326/99).
Multidisciplinary Guideline: Other involved WP: BWP, SWP, PhvWP.
Action: EWP contribution to the BWP. Released for 6-month consultation in November 2002 for
comments by May 2003. Finalisation expected 3Q2003.
•
Note for Guidance on Comparability of Medicinal Products containing biotechnology-derived
proteins as active substance.
Multidisciplinary Guideline: Other involved WP: BWP
Action: Released for 6-month consultation in July 2002
•
Nfg on the use of medicinal products during pregnancy: need for post-marketing data
(CPMP/EWP/PhVWP/1417/01).
Multidisciplinary Guideline: Other involved WP: PhVWP
Action: Release for consultation expected 2003
•
Nfg on risk assessment of medicinal products on human reproductive and development toxicities:
from data to labelling. (CPMP/SWP/373/01).
Multidisciplinary Guideline: Other involved WP: SWP
Action: Concept paper adopted in June 2001.
•
NfG on the clinical requirements for locally applied, locally acting products containing known
constituents (CPMP/EWP/239/95)
•
NfG on clinical trials with haematopoietic growth factors for the prophylaxis of infection following
myelosuppressive or myeloablative therapy (CPMP/EWP/555/95)
•
NfG on fixed combination medicinal products (CPMP/EWP/240/95)
•
MEDDEV guideline
Action: EWP contribution in considreation
III.
ICH Guidelines and activities
•
E5: Ethnic Factors
Action: Questions and answers document to be prepared for 1Q2003.
•
Clinical Guidance on Assessing QT Prolongation potential
Action: Document to be prepared for 1Q2003.
IV.
EU Regulatory Activities
•
Notice to Applicant (CTD — ICH M4)
Action: Follow-up of the implementation of the CTD.
•
Guideline on SPC
Multidisciplinary Guideline: EC, PhWP, SWP, QWP, BWP, QRD group
Action: Contribution to expected revision in 2003.
•
EC guidelines relating to the implementation of the Clinical Trial Directive
Action: Follow-up of the EWP contribution.
•
Annex I of Directive 2001/83/EC
Action: Follow-up of the EWP contribution
V.
Activities with external parties
1.
Drug Regulatory Authorities Outside the EU (excluding ICH activities, already mentioned):
-
PERF III activities.
-
Liaison with FDA or other Agencies.
2.
Meeting with Interested Parties (e.g. Learned Societies, Public health Stake Holders (Public
health professionals, Patients’ organisations, ...), Pharmaceutical Industry Representatives).
-
Workshop with learned Societies to be considered once a year, in addition to improvement of written
communication to favour Learned Societies’ input in draft EWP guidance.
Meeting with EFPIA and/or other Pharmaceutical Industry Representative once/twice a year, upon request.
-
VI.
Organisational matters
1.
List of adopted organisational documents (e.g. mandate, template, SOP)
-
Recommendation for drafting CPMP/EWP clinical guidelines (CPMP/EWP/903/96, rev. 1),
-
Procedure for developing CPMP guidelines and points to consider documents (CPMP/2024/98),
-
Revision of the internal organisation of the EWP (dated February 2001),
-
Recommendation of the follow-up of EWP Points to consider/Note for guidance (CPMP/EWP/36/00, 16
March 2001),
-
Ongoing revision and update of the EWP Workplan,
-
EWP templates of concept paper, Points to consider and Note for Guidance with explanatory note
(EMEA/8249/02).
Efficacy Working Party (EWP)
Adopted Guidelines
CPMP/EWP/18/01 Note for Guidance on the Clinical Investigation of Medicinal Products in the
Treatment of Urinary Incontinence in Women (CPMP adopted December 2002)
CPMP/EWP/612/00 Note For Guidance on the Clinical Investigation of Medicinal Products for
Treatment of Nociceptive Pain (CPMP adopted November 2002)
CPMP/EWP/2922/01 Note for Guidance on the Clinical Investigation of Medicinal Products in the
treatment of Asthma (CPMP adopted November 2002)
CPMP/EWP/205/95 Revision 2 Note for Guidance on Evaluation of Anticancer Medicinal Products
in Man (CPMP adopted September 2002)
see also CPMP/EWP/569/02 draft Addendum on Paediatric Oncology (CVMP released for
consultation September 2002)
CPMP/EWP/282/02 Position Paper on the Regulatory Requirements for the Authorisation of Llowdose Modified Release asa Formulations in the Secondary Prevention of Cardiovascular Events
(Final)
CPMP/EWP/1080/00 Note For Guidance on Clinical Investigation of Medicinal Products in the
Treatment of Diabetes Mellitus (CPMP adopted May 2002)
CPMP/EWP/518/97 Revision 1 Note For Guidance on Clinical Investigation of Medicinal Products
in the Treatment of Depression (CPMP adopted April 2002)
CPMP/EWP/714/98 Revision 1 Note for Guidance on the Clinical Investigation of Medicinal
Products in the Treatment of Peripheral Arterial Occlusive Disease (CPMP adopted April 2002)
CPMP/180/95 Guideline for PMS Studies for Metered Dose Inhalers with New Propellants
٧
CPMP/EWP/2747/00 Note for Guidance on Co-ordinating Investigator Signature of Clinical Study
Reports (Adopted October 2001)
CPMP/EWP/561/98 Note for Guidance on Clinical Investigation of Medicinal Products for
theTreatment of Multiple Sclerosis (Adopted July 2001)
CPMP/EWP/QWP/1401/98 Note For Guidance on the Investigation of Bioavailability and
Bioequivalence (Adopted July 2001)
CPMP/EWP/567/98 Note for Guidance on Clinical Investigation of Medicinal Products for Bipolar
Disorder (CPMP adopted April 2001)
CPMP/EWP/552/95 Revision 1 Note for Guidance on Post Menopausal Osteoporosis in Women
(CPMP adopted January 2001)
CPMP/EWP/566/98 Revision 1 Note for Guidance on Clinical Investigation of Medicinal Products in
the Treatment of Epilectic Disorders (CPMP adopted November 2000)
CPMP/EWP/519/98 Note for Guidance on Clinical Investigation of Steroid Contraceptives in
Women
CPMP/EWP/235/95 Revision 1 Note for Guidance on the Clinical Investigation of Medicinal
Products in the Treatment of Cardiac Failure (CPMP adopted Dec. 99)
CPMP/EWP/563/98 Note for Guidance on Clinical Investigation of Medicinal Products for the
Treatment of Venous Thromboembolic Disease (CPMP adopted Dec. 99)
CPMP/EWP/280/96 Note For Guidance on Modified Release Oral and Transdermal Dosage
Forms: Section II (PharmacoKinetic and Clinical Evaluation)
CPMP/EWP/463/97 Note for guidance on Clinical Evaluation of New Vaccines
CPMP/EWP/563/95 Note for guidance on Clinical Investigation of Medicinal Products in the
Treatment of Parkinson's Disease
CPMP/EWP/238/95 Revision 1 Note for Guidance on Clinical Investigation on Medicinal Products
in the Treatment of Hypertension (CPMP adopted May 97, revised November 98)
CPMP/EWP/559/95 Note for guidance on the Clinical Investigation of Medicinal Products in the
Treatment of Schizophrenia (CPMP adopted Feb. 98)
CPMP/EWP/281/96 Note for Guidance on the Clinical Investigation of Drug used for Weight
Control (CPMP adopted Dec. 97)
CPMP/EWP/560/95 Note for Guidance on the Investigation of Drug Interactions (CPMP adopted
Dec. 97)
CPMP/EWP/553/95 Note for Guidance on Medicinal Products in the Treatment of Alzheimer's
Disease (CPMP adopted July 97)
CPMP/EWP/520/96 Note for Guidance on the Pharmacodynamic section of the SPC for AntiBacterial Medicinal Products (CPMP adopted June 97)
CPMP/EWP/234/95 Note for guidance on the clinical investigation of anti-anginal medicinal
products in stable angina pectoris
CPMP/EWP/558/95 Note for Guidance on Evaluation of new anti-Bacterial Medicinal Products
(CPMP adopted April 97)
CPMP/EWP/462/95 Note for Guidance on Clinical Investigation of Medicinal Products in Children
(CPMP adopted March 97)
CPMP/EWP/240/95 Note for Guidance on Fixed Combination Medicinal Products (CPMP adopted
April. 96)
CPMP/EWP/555/95 Note for Guidance on Clinical trials with Haematopoietic Growth factors for
the Prophylaxis of Infection following Myelosuppressive or Myeloablative Therapy (CPMP adopted
March 96)
CPMP/EWP/237/95 Note for Guidance on Antiarrhythmics (CPMP adopted Nov.95)
CPMP/EWP/239/95 Note for Guidance on the Clinical Requirements for Locally Applied, Locally
Acting Products containing Known Constituents (CPMP adopted Nov.95)
• Workplan •
• Concept Papers • Points to Consider • Draft Guidelines • Adopted Guidelines •
© 1995-2003 EMEA
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Draft Guidelines
CPMP/EWP/967/01 Note for Guidance on the Evaluation of Medicinal Products indicated for
Thrombolysis in Acute Myocardial Infarction (AMI) (Released for consultation November 02)
CPMP/EWP/788/01 Note for Guidance on the Clinical Investigation of Medicinal Products for
treatment of Migraine (Released for consultationSeptember 02)
CPMP/3097/02 Note for Guidance Consultation on Comparability of Medicinal Products containing
Biotechnology-deriverd Proteins as Drug Substance (Released for consultation July 02)
CPMP/EWP/633/02 Note for Guidance on Clinical Development of Medicinal Products for the
Treatment of HIV Infection (Released for consultation July 02)
CPMP/EWP/49/01 Appendix to the Committee for Proprietary Medicinal Products (CPMP) Note for
Guidance on the Clinical Investigation of Medicinal Products in the Treatment of Schizophrenia, on
the Methodology of Clinical Trials concerning the Development of Depot Preparations of Approved
Medicinal Products in Schizophrenia (Released for consultation February 02)
• Workplan •
© 1995-2002 EMEA
Send all queries regarding the Web service to: Webmaster
Points to Consider
٧
٧
٧
٧
٧
CPMP/EWP/908/99 CPMP Points to Consider on Multiplicity issues in Clinical Trials (CPMP
Adopted September 2002)
CPMP/EWP/1343/01 Points to Concider of new Anti-Fungal Agents for Invasive Fungal Infections
(Released for consultation Julyl 02)
CPMP/EWP/785/97 Points to Consider on the Evaluation of Medicinal Products for the Treatment
of Irritable Bowel Syndrome (Released for consultation April 02)
CPMP/602/95 Rev. 3 Points to consider on the Assessment of Anti-HIV Medicinal Products
(Adopted by CPMP in December 2001)
CPMP/EWP/4151/00 Points to Consider on the Requirements for Clinical Documentation for
Metered Dose Inhalers (MDI)(Released for consultation January 02)
CPMP/EWP/2863/99 Points to Consider on Adjustment for baseline Covariates (Released for
Consultation December 2001)
CPMP/EWP/1776/99 Points to Consider on Missing Data ( Adopted November 2001)
CPMP/EWP/1119/98 Points to Consider on the Evaluation of the diagnostic agents ( Adopted
November 2001)
CPMP/EWP/560/98 Points to Consider on Clinical Investigation of Medicinal Products for
theTreatment of Acute Stroke ( Adopted September 2001)
CPMP/EWP/2284/99 Points to Consider on Clinical Investigation of Medicinal Products for the
Management of Crohn's Disease (adopted by CPMP June 2001)
CPMP/2330/99 Points to Consider on Application with 1.) Meta-analyses and 2.) One Pivotal study
(adopted by CPMP May 2001)
CPMP/EWP/565/98 Points to Consider on Clinical Investigation of Medicinal Products for
theTreatment of Amyotrophic Lateral Sclerosis (Adopted October 2000)
CPMP/EWP/482/99 Points to Consider on Switching between Superiority and Noninferiority (Adopted July 2000)
CPMP/EWP/2655/99 Points to Consider on Pharmacokinetics and Pharmacodynamics in the
Development of Antibacterial Medicinal Products (Adopted July 2000)
CPMP/EWP/707/98 Points to Consider on Clinical Investigation of Medicinal Products fo
Prophylaxis of Intra- and Post- operative Venous Thromboembolic Risk (CPMP approved June
2000)
CPMP/EWP/570/98 Points to Consider on Clinical Investigation of New Medicinal Products for the
treatment of Acute Coronary syndrome (ACS) without persistent ST-Segment Elevation
CPMP/EWP/197/99 Points to Consider concerning Endpoints in Clinical Studies with
Haematopoietic Growth Factors for Mobilisation of Autologous Stem Cells
CPMP/EWP/863/98 Points to Consider on Wording of Helicobacter Pylori Eradication Therapy in
selected SPC Sections
CPMP/EWP/562/98 Points to Consider on Clinical Investigation of Medicinal Products in the
Treatment of Patients with Chronic Obstructive Pulmonary Disease (COPD)
CPMP/EWP/556/95 Points to Consider on the Clinical Investigation of Slow-Acting Anti-Rheumatic
Medicinal Products in Rheumatoid Arthritis
CPMP/EWP/784/97 Points to Consider on Clinical Investigation of Medicinal Products used in the
Treatment of Osteoarthritis
CPMP/EWP/021/97 Points to Consider on Hormone Replacement Therapy.(of 19 November
1997)
CPMP/EWP/504/97 Points to Consider on Clinical investigation of Medicinal Products in the
Treatment of Patients with Acute Respiratory Distress Syndrome.(of 9 October 1997)
• Workplan •
© 1995-2003 EMEA
Send all queries regarding the Web functionality to: EMEAwebservices
Concept Papers
٧
٧
CPMP/EWP/4279/02 Concept Paper on the Development of a CPMP Note for Guidance on
Clinical Investigation of Medicinal Products for the Treatment of Obsessive Compulsive Disorder
Clinical Investigation of Medicinal Products for the Treatment of Panic Disorder
Clinical Investigation of Medicinal Products for the Treatment of General Anxiety Disorder
CPMP/EWP/2459/02 Concept Paper on the Development of a CPMP Points to Concider on
Methodological Issues in Confirmatory Clinical Trials with Flexible Design and Analysis Plan
Evaluation of the Pharmacokinetics of Medicinal Products in Patients with Hepatic Impairment
Clinical Investigation of medicinal products ot the Treatment of Psoriasis
CPMP/EWP/2455/02 Concept Paper on the Development of a CPMP Points to Consider on
Allergic Rhino-conjunctivitis
CPMP/EWP/968/02 Concept Paper on the Development of a CPMP Points to Consider on the
Evaluation of the Pharmacokinetics of Medicinal Products in the Paediatric Population
CPMP/EWP/226/02 Concept Paper on the Development of a CPMP NfG on the Clinical
Pharmacokinetic investigation of the Pharmacokinetics of Peptides and Proteins
CPMP/EWP/225/02 Concept Paper on the Development of a CPMP NfG on the Evaluation of the
Pharmacokinetics of Medicinal Products in Patients with Impaired Renal Function
CPMP/EWP/1412/01 Concept Paper on the Development of the Revision of the CPMP NfG on
Evaluation of New Anti-bacterial Medicinal Products (CPMP/EWP/558/95) and the CPMP NfG on
the Pharmacodynamic Section of the SPC for Anti-bacterial Medicinal Products
(CPMP/EWP/520/96)
CPMP/EWP/2991/01 Concept Paper on the Development of an Addendum the Clinical
Requirements of Modified Release Medicinal Products submitted as a Line Extension of an
existing Marketing Authorization to the CPMP Note for Guidance on Modified Release Oral and
Transdermal Dosage Forms: Section II (Pharmacokinetic and Clinical
Evaluation)(CPMP/EWP/280/96)
CPMP/EWP/1533/01 Concept Paper on the Development of an Addendum on Acute Cardiac
Failure to the CPMP Note for Guidance on Clinical Investigation of Medicinal Products in the
Treatment of Acute cardiac Failure
CPMP/EWP/PHVWP/1417/01 Concept Paper on the Development of a Committee for Proprietary
Medicinal Products (CPMP) Note for Guidance on the Use of Medicinal Products during
Pregnancy: Need for Post-Marketing Data
CPMP/EWP/512/01 Concept Paper on the Development of a Committee for Proprietary Medicinal
Products (CPMP) Note for Guidance on the Evaluation of Medicinal Products for the treatment of
Dyslipoproteinaemia
CPMP/EWP/2158/99 Concept Paper on the Development of a Committee for Proprietary Medicinal
Products (CPMP) Points to Consider on Biostatistical/Methodological issues arising from recent
CPMP discussions on Licensing Applications: Choice of Delta
• Workplan •
© 1995-2003 EMEA
ICH
•
•
•
INTERNATIONAL CONFERENCE OF HARMONIZATION OF
TECHNICAL REQUIREMENTS FOR REGISTRATION OF
PHARMACEUTICALS FOR HUMAN USE.
IT IS A UNIQUE PROJECT THAT BRINGS TOGETHER THE
REGULATORY AUTHORITIES OF EUROPE, JAPAN, UNITED
STATES AND EXPERTS FROM THE PHARMACEUTICAL
INDUSTRY IN THE THREE REGIONS TO DISCUSS
SCIENTIFIC AND TECHNICAL ASPECTS OF PRODUCT
REGISTRATION.
THE PURPOSE IS TO MAKE RECOMMENDATIONS ON
WAYS TO ACHIEVE GREATER HARMONIZATION IN THE
INTERPRETATION AND APPLICATION OF TECHNICAL
GUIDELINES AND REQUIREMENTS FOR PRODUCT
REGISTRATION IN ORDER TO REDUCE OR OBVIATE THE
NEED TO DUPLICATE THE TESTING CARRIED OUT DURING
THE RESEARCH AND DEVELOPMENT OF NEW MEDICINES.
HISTORY OF ICH
•
•
•
•
•
TRIGGERS FOR SETTING UP THE PRODUCT
AUTHORIZATION SYSTEM:
- USA => TRAGIC MISTAKE IN THE FORMULATION OF A
CHILDREN’S SYRUP IN THE 1930s (=>FDA)
- JAPAN => NEED FELT IN THE 1950s (=> MHLW)
- EUROPE => THALIDOMIDE TRAGEDY IN THE 1960s (=>
EMEA)
THE 1960s AND 1070s SAW A RAPID INCREASE IN LAWS,
REGULATIONS AND GUIDELINES FOR REPORTING AND
EVALUATING THE DATA ON SAFETY, QUALITY AND
EFFICACY OF NEW MEDICINAL PRODUCTS.
AT THE WHO CONFERENCE OF DRUG REGULATORY
AUTHORITIES (ICDRA) IN PARIS IN 1989 SPECIFIC PLANS
FOR ACTION BEGAN TO MATERIALIZE
THE BIRTH OF ICH TOOK PLACE AT A MEETING IN APRIL
1990, HOSTED BY EFSPIA IN BRUSSELS
THE MEETING AT SAN DIEGO IN 2000 MARKS THE END OF
10 YEARS OF ACTIVITY.
STRUCTURE OF ICH
•
•
•
•
ICH STEERING COMMITTEE: ESTABLISHED IN 1990 HAS
MET AT LEAST TWICE A YEAR WITH LOCATION ROTATING
BETWEEN THE TRHEE REGIONS.
SIX-PARTY EXPERT WORKING GROUPS (EWG): FOR
EVERY NEW TOPIC, EACH OF THE SIX ICH PARTIES (EC,
EFPIA, MHLW, JPMA, FDA, PhRMA) DESIGNATE A TOPIC
LEADER, WHO WILL PARTICIPATE IN THE EWG MEETINGS
SOMETIMES MEMBERSHIP OF EWG CAN BE EXTENDED
TO OTHER INTERESTED PARTIES
THREE OBSERVERS THAT NOMINATE AN EXPERT TO THE
EWG
TOPICS FOR HARMONIZATION
ARE DEVIDED INTO:
SAFETY
QUALITY
EFFICACY
MAJOR TOPICS: MINOR TOPICS:
- ABBREVIATED
- FULL ICH
MAINTENANCE
PROCESS
PROCESS
BASED ON A
FIVE-STEP
APPROACH
FULL ICH PROCESS
•
•
•
•
•
STEP 1: CONSENSUS BUILDING
- SIGN-OFF BY EXPERT WORKING GROUPS MEMBERS
STEP 2: START OF REGULATORY ACTION
- REACHED WHEN THE STEERING COMMITTEE AGREES, ON THE
BASIS OF THE REPORT FROM THE EWG, THAT THERE IS
SUFFICIENT SCIENTIFIC CONSENSUS ON THE TECHNICAL
ISSUES FOR THE GUIDELINE TO PROCEED TO THE NEXT STAGE
OF REGULATORY CONSULTATION
STEP 3: REGULATORY CONSULTATION
- THE GUIDELINE LEAVES THE ICH PROCESS AND BECOMES THE
SUBJECT OF NORMAL WIDE-RANGING CONSULTATION IN THE
THREE REGIONS. IT IS PRINTED AS A DRAFT (BY CPMP, FDA AND
MHLW) FOR INTERNAL AND EXTERNAL REVIEW
STEP 4: ADOPTION OF A TRIPARTITE HARMONIZED TEXT
- THE GUIDELINE RETURNS TO THE ICH FORUM. IF BOTH
REGULATORY AND INDUSTRY PARTIES ARE SATISFIED THAT
THE CONSENSUS ACHIEVED AT STEP 2 IS NOT SUBSTANTIALLY
ALTERED AS RESULT OF THE CONSULATION, THE TEXT IS
ADOPTED BY THE STEERING COMMITTEE
STEP 5: IMPLEMENTATION
International Conference on Harmonization (ICH)
Efficacy - Adopted Guidelines
٧
٧
٧
٧
٧
٧
Topic E 2B(M) Step 5 Modification Note for Guidance on Clinical Safety Data Management: Data
Elements for Transmission of Individual Case Safety Reports (ICH ICSR DTD Version
2.3)(CPMP/ICH/287/95 - Mod. released foir information November 2000)
Topic E11 Step 4 Note for Guidance on Clinical Investigation of Medicinal Products in the
Paediatric Population.(CPMP/ICH/2711/99 - adopted July 2000)
Topic E10 Step 4 Note for Guidance on Choice of Control Group for Clinical Trials
(CPMP/ICH/364/96 - adopted July 2000)
Topic E9 Step 4 Note for Guidance on Statistical Principles for Clinical Trials.(CPMP/ICH/363/96 adopted Mar.98)
Topic E5 Step 4 Note for Guidance on Ethnic Factors in the Acceptability of Foreign Clinical
Data.(CPMP/ICH/289/95 - adopted Mar.98)
Topic E8 Step 4 Note for Guidance on General Considerations for Clinical
Trials(CPMP/ICH/291/95 - adopted Sept. 97)
Topic E6 Step 5 Note for Guidance on Good Clinical Practice(CPMP/ICH/135/95 - adopted July
96)
Explanatory Note and Comments to CPMP/ICH/135/95
Topic E2C Step 4 Note for Guidance on Clinical Safety Data Management: Periodic Safety Update
Reports for Marketed Drugs (CPMP/ICH/288/95 - adopted December 96)
Topic E2A Step 5 Note for Guidance on Good Clinical Safety Data Management: Definitions and
Standards for Expedited Reporting (CPMP/ICH/377/95 - adopted November 94)
Topic E1A Step 5 Note for Guidance on Population Exposure: The extent of Population Exposure
to assess Clinical Safety (CPMP/ICH/375/95 adopted November 94)
Topic E4 Step 5 Note for Guidance on Dose Response Information to support Drug Registration
(CPMP/ICH/378/95 - adopted May 94)
Topic E7 Step 5 Note for Guidance on Studies in support of Special Populations: Geriatrics(
CPMP/ICH/379/95 - adopted Sept. 93)
Topic E3 Step 4 Note for Guidance on Structure and Content of Clinical Study
Reports(CPMP/ICH/137/95 - adopted Dec. 95)
Annex I
Annex II
Annex III
Annex IVa
Annex IVb
Annex V
Annex VI
Annex VII
Annex VIII
© 1995-2001 EMEA
International Conference on Harmonization (ICH)
Efficacy - Draft Guidelines
Topic V1* Clinical Safety Data Management Periodic Safety Update Reports for Marketed Drugs
(CPMP/ICH/4679/02 - released for consultation Sept.2002) (Addendum to ICH E2C)
Topic E 12 A (Draft Consensus) Principles for Clinical Evaluation of new Antihypertensive Drugs
(CPMP/ICH/541/00 - released for consultation Jun.00)
* Provisional Designation
© 1995-2002 EMEA
Guidance Documents
Guidance documents represent the Agency's current thinking on a particular subject. They do not create or confer any
rights for or on any person and do not operate to bind FDA or the public. An alternative approach may be used if such
approach satisfies the requirements of the applicable statute, regulations, or both. For information on a specific guidance
document, please contact the originating office.
Most of these documents are in Adobe Acrobat format
, also known as PDF. The free upgrade to Adobe Acrobat 4.0
or higher is recommended, especially if you have difficulty opening any of the documents below. Another method of
obtaining guidance documents is through the Division of Drug Information.
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CDER Guidance Documents
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•
•
•
•
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FDA's Good Guidance Practices regulation of September 19, 2000. Optional Format: PDF.
Comprehensive List of Guidance Documents
(3/11/2003)
Guidance Agenda: Guidances CDER is Planning to Develop During Fiscal Year 2003 (10/21/2002)
New/Revised/Withdrawn List for 2000
New/Revised/Withdrawn List
(5/24/2001)
(9/23/2002)
Advertising
1.
2.
3.
Aerosol Steroid Product Safety Information in Prescription Drug Advertising and Promotional Labeling
(Issued 12/1997, Posted 1/12/1998)
Consumer-Directed Broadcast Advertisements [HTML] or [PDF] (Issued 8/1999, Posted 8/6/1999)
Questions and Answers (Posted 8/6/1999)
Industry-Supported Scientific and Educational Activities [HTML] or [PDF] (Issued 12/3/1997, Posted 12/4/1997)
Advertising Draft
1.
Accelerated Approval Products: Submission of Promotional Materials
2.
(Issued 1/1999,
Product Name Placement, Size, and Prominence in Advertising and Promotional Labeling
Posted 3/12/1999)
Promoting Medical Products in a Changing Healthcare Environment; I. Medical Product Promotion by
3.
4.
(Posted 3/26/1999)
Healthcare Organizations or Pharmacy Management Companies (PBMs)
(Issued 12/1997. Posted 1/5/1998)
Using FDA-Approved Patient Labeling in Consumer-Directed Print Advertisements [HTML] or [PDF] (Issued
4/2001, Posted 4/20/2001]
Biopharmaceutics
1.
2.
Bioanalytical Method Validation [HTML] or [PDF] (Issued 5/2001, Posted 5/22/2001)
Bioavailability and Bioequivalence Studies for Orally Administered Drug Products - General Considerations
[Word] or [PDF] (Issued 3/2003, Posted 3/19/2003)
3.
Cholestyramine Powder in Vitro Bioequivalence (Intermin Guidance)
4.
Clozapine Tablets in Vivo Bioequivalence and in Vitro Dissolution Testing
10/15/1998)
(Issued 11/15/1996, Reposted
5.
Corticosteroids, Dermatologic (topical) In Vivo
6.
Dissolution Testing of Immediate Release Solid Oral Dosage Forms
or WordPerfect 6.x Version (Issued
8/1997, Posted 8/25/1997)
Extended Release Oral Dosage Forms: Development, Evaluation, and Application of In Vitro/In Vivo
7.
8.
(Issued 6/2/1995, Posted 3/6/1998)
Correlations
(Issued 9/1997, Posted 9/26/1997)
Food-Effect Bioavailability and Fed Bioequivalence Studies [Word] or [PDF] (Issued 12/2002, Posted
1/30/2003)
(Issued 6/27/1989, Posted 3/2/1998)
9. Metaproterenol Sulfate and Albuterol Metered Dose Inhalers In Vitro
10. Phenytoin/Phenytion Sodium (capsules, tablets, suspension) In Vivo Bioequivalence and In Vitro Dissolution
Testing
(Issued 3/4/1994, Posted 3/2/1998)
11. Potassium Chloride (slow-release tablets and capsules) In Vivo Bioequivalence and In Vitro Dissolution Testing
(Revised 6/6/1994, Posted 6/22/1998)
12. Statistical Approaches to Establishing Bioequivalence [HTML] or [PDF] (Issued 2/2001, Posted 2/1/2001)
13. Waiver of In Vivo Bioavailability and Bioequivalence Studies for Immediate-Release Solid Oral Dosage Forms
Based on a Biopharmaceutics Classification System. Optional Format: PDF. (Issued 8/2000, Posted
8/31/2000)
Biopharmaceutics (Draft)
1.
2.
3.
4.
Bioavailability and Bioequivalence Studies for Nasal Aerosols and Nasal Sprays for Local Action [Word] or
[PDF] (Posted 4/2/2003)
o Statistical Information from the June 1999 Draft Guidance and Statistical Information for In Vitro
Bioequivalence Data
(Posted 4/11/2003)
Conjugated Estrogens, USP-LC-MS Method for Both Qualitative Chemical Characterization and Documentation
of Qualitative Pharmaceutical Equivalence [HTML] or [PDF] (Issued 3/6/2000, Posted 3/6/2000)
In Vivo Bioequivalence Studies Based on Population and Individual Bioequivalence Studies
(Issued
12/10/1997, Posted 12/10/1997) Bioequivalence Studies Data and Detailed Statistical Methodology (Posted
3/2/1998)
Potassium Chloride Modified-Release Tablets and Capsules: In Vivo Bioequivalence and In Vitro Dissolution
Testing
(Issued 8/2002, Posted 8/6/2002)
Chemistry
1.
2.
3.
4.
5.
6.
7.
8.
9.
BACPAC I: Intermediates in Drug Substance Synthesis; Bulk Actives Postapproval Changes: Chemistry,
Manufacturing, and Controls Documentation [HTML] or [PDF] (Issued 2/2001, Posted 2/16/2001)
Changes to an Approved Application for Specified Biotechnology and Specified Synthetic Biological Products
(Issued 7/1997, Posted 7/28/1997)
Changes to an Approved NDA or ANDA [HTML] or [PDF] (Issued 11/1999, Posted 11/19/1999)
Changes to an Approved NDA or ANDA: Questions and Answers [HTML] or [PDF] (Issued 1/2001, Posted
1/22/2001)
Container Closure Systems for Packaging Human Drugs and Biologics [HTML] or [PDF] (Issued 5/1999, Posted
7/6/1999)
o Container Closure Systems for Packaging Human Drugs and Biologics -- Questions and Answers
[PDF] (Issued 5/2002, Posted 5/10/2002)
Demonstration of Comparability of Human Biological Products, Including Therapeutic Biotechnology-derived
Products
Development of New Stereoisomeric Drugs (5/1/1992) (Post Date: 1/3/1996)
Drug Master Files (9/1/1989)
Current DMF Information (e.g. lists, addresses, etc.)
Drug Master Files for Bulk Antibiotic Drug Substances [PDF] or [Word] (Issued 11/1999, Posted 11/26/1999)
10. Environmental Assessment of Human Drug and Biologics Applications
(Issued 7/1998, Posted 7/24/98)
11. Format and Content of the Chemistry, Manufacturing and Controls Section of an Application*
2/1987, Posted 3/2/1998)
(Issued
12. Format and Content for the CMC Section of an Annual Report (9/1/1994)
13. IND Meetings for Human Drugs and Biologics Chemistry, Manufacturing, and Controls Information [HTML] or
14. Monoclonal Antibodies Used as Reagents in Drug Manufacturing [HTML] or [PDF] (Issued 3/2001, Posted
3/28/2001)
15. Nasal Spray and Inhalation Solution, Suspension, and Drug Products [HTML] or [PDF] (Issued 7/2002, Posted
7/3/2002)
16. NDAs: Impurities in Drug Substances [HTML] or [PDF] (Issued 2/2000, Posted 2/24/2000)
(Issued 4/28/1998, Posted
17. PAC-ATLS: Postapproval Changes - Analytical Testing Laboratory Sites
4/28/1998)
18. The Sourcing and Processing of Gelatin to Reduce the Potential Risk Posed by Bovine Spongiform
Encephalopathy (BSE) (12/20/2000)
19. SUPAC-IR: Immediate-Release Solid Oral Dosage Forms: Scale-Up and Post-Approval Changes: Chemistry,
Manufacturing and Controls, In Vitro Dissolution Testing, and In Vivo Bioequivalence Documentation
20. SUPAC-IR Questions and Answers about SUPAC-IR Guidance (2/18/1997)
21. SUPAC-IR/MR: Immediate Release and Modified Release Solid Oral Dosage Forms Manufacturing Equipment
Addendum
(Issued 1/1999, Posted 2/25/1999)
22. SUPAC-MR: Modified Release Solid Oral Dosage Forms Scale-Up and Postapproval Changes: Chemistry,
Manufacturing, and Controls; In Vitro Dissolution Testing and In Vivo Bioequivalence Documentation
10/6/1997, Posted 10/6/1997)
23. SUPAC-SS: Nonsterile Semisolid Dosage Forms; Scale-Up and Post-Approval Changes: Chemistry,
Manufacturing and Controls; In Vitro Release Testing and In Vivo Bioequivalence Documentation
5/1997; Posted 6/16/1997)
(Issued
(Issued
24. Reviewer Guidance, Validation of Chromatographic Methods
25. Submission Documentation for Sterilization Process Validation in Applications for Human and Veterinary Drug
Products
26. Submission of Chemistry, Manufacturing, and Controls Information for Synthetic Peptide Substances
27. Submitting Documentation for the Manufacturing of and Controls for Drug Products* [HTML] or [PDF] (Issued
2/1987, Posted 3/2/1998)
28. Submitting Documentation for the Stability of Human Drugs and Biologics*
29. Submitting Samples and Analytical Data for Methods Validation
(Issued 2/1987, Posted 3/2/1998)
30. Submitting Supporting Documentation in Drug Applications for the Manufacture of Drug Substances
Chemistry (Draft)
1.
2.
3.
Analytical Procedures and Methods Validation. Optional format: PDF. (Issued 8/2000, Posted 8/30/2000)
Botanical Drug Products. Optional format: PDF. (Issued 8/2000, Posted 8/10/2000)
Comparability Protocols -- Chemistry, Manufacturing, and Controls Information [Word] or [Acrobat] (Issued
2/2003, Posted 2/20/2003)
4.
5.
Drug Product: Chemistry, Manufacturing, and Controls Information
(Issued 1/2003, Posted 1/28/2003)
INDs for Phase 2 and 3 Studies of Drugs, Including Specified Therapeutic Biotechnology-Derived Products
7.
Chemistry, Manufacturing, and Controls Content and Format
(Issued 2/1999, Published 4/19/1999)
Metered Dose Inhaler (MDI) and Dry Powder Inhaler (DPI) Drug Products [HTML] or [PDF] or (Issued
11/13/1998, Posted 11/19/1998, HTML Posted 9/27/1999)
Liposome Drug Products: Chemistry, Manufacturing, and Controls; Human Pharmacokinetics and
8.
Stability Testing of Drug Substances and Drug Products
9.
SUPAC-SS: Nonsterile Semisolid Dosage Forms Manufacturing Equipment Addendum
Posted 1/5/1999)
6.
Bioavailability; and Labeling Documentation.
(Issued 7/2002, Posted 8/20/2002)
(Issued 6/5/1998, Posted 6/8/1998)
(Issued 12/1998,
Clinical/Antimicrobial
1.
2.
Antiretroviral Drugs Using Plasma HIV RNA Measurements — Clinical Considerations for Accelerated and
Traditional Approval [Word] or [PDF] (Issued 10/2002, Posted 10/31/2002)
Clinical Development and Labeling of Anti-Infective Drug Products [HTML] or [PDF] (Issued 10/1992, Posted
3/2/1998, Revised 2/12/2001)
3.
Clinical Evaluation of Anti-Infective Drugs (Systemic)
4.
Preclinical Development of Antiviral Drugs
(Issued 9/77, Posted 3/2/1998)
Clinical/Antimicrobial (Draft)
1.
Acute Bacterial Exacerbation of Chronic Bronchitis — Developing Antimicrobial Drugs for Treatment
7/22/1998, Posted 7/22/1998)
(Issued
2.
Acute Bacterial Meningitis — Developing Antimicrobial Drugs for Treatment
7/22/1998)
3.
Acute Bacterial Sinusitis — Developing Antimicrobial Drugs for Treatment
7/22/1998)
4.
Acute or Chronic Bacterial Prostatitis — Developing Antimicrobial Drugs for Treatment
Posted 7/22/1998)
5.
Acute Otitis Media — Developing Antimicrobial Drugs for Treatment
6.
7.
(Issued 7/22/1998, Posted 7/22/1998)
Bacterial Vaginosis — Developing Antimicrobial Drugs for Treatment
Catheter-Related Bloodstream Infections - Developing Antimicrobial Drugs for Treatment [HTML] or [PDF]
(Issued 10/1999, Posted 10/18/1999)
8.
(Issued 7/22/1998,
Community-Acquired Pneumonia — Developing Antimicrobial Drugs for Treatment
Posted 7/22/1998)
Complicated Urinary Tract Infections and Pyelonephritis — Developing Antimicrobial Drugs for Treatment
(Issued 7/22/1998, Posted 7/22/1998) [HTML] or [PDF]
9.
(Issued 7/22/1998,
(Issued 7/22/1998, Posted 7/22/1998)
10. Developing Antimicrobial Drugs — General Considerations for Clinical Trials
7/22/1998) [Main Document]
11. Empiric Therapy of Febrile Neutropenia — Developing Antimicrobial Drugs for Treatment
Posted 7/22/1998)
(Issued 7/22/1998,
12. Evaluating Clinical Studies Of Antimicrobials In The Division Of Anti-Infective Drug Products (2/18/1997)
13. Inhalational Anthrax (Post Exposure) -- Developing Antimicrobial Drugs (Issued 3/15/2002, Posted 3/15/2002)
[HTML] or [PDF]
14. Lyme Disease — Developing Antimicrobial Drugs for Treatment
(Issued 7/22/1998, Posted 7/22/1998)
15. Nosocomial Pneumonia — Developing Antimicrobial Drugs for Treatment
7/22/1998)
16. Secondary Bacterial Infections of Acute Bronchitis — Developing Antimicrobial Drugs for Treatment
7/22/1998, Posted 7/22/1998)
(Issued
(Issued 7/22/1998,
17. Streptococcal Pharyngitis and Tonsillitis — Developing Antimicrobial Drugs for Treatment
Posted 7/22/1998)
18. Uncomplicated and Complicated Skin and Skin Structure Infections — Developing Antimicrobial Drugs for
Treatment
(Issued 7/22/1998, Posted 7/22/1998)
19. Uncomplicated Gonorrhea — Developing Antimicrobial Drugs for Treatment
7/22/1998)
20. Uncomplicated Urinary Tract Infections — Developing Antimicrobial Drugs for Treatment
Posted 7/22/1998)
21. Vulvovaginal Candidiasis — Developing Antimicrobial Drugs for Treatment
7/22/1998)
(Issued 7/22/1998,
٧ Clinical/Medical
1.
2.
3.
Acceptance of Foreign Clinical Studies [HTML] or [PDF] (Posted 3/12/2001)
Cancer Drug and Biological Products - Clinical Data in Marketing Applications [HTML] or [PDF] (Posted
10/11/2001)
Clinical Development Programs for Drugs, Devices, and Biological Products for the Treatment of Rheumatoid
Arthritis (RA) (Issued 1/1999, Posted 2/16/1999) [HTML] or [PDF]
4.
Clinical Development Programs for MDI and DPI Drug Products
5.
Clinical Evaluation of Analgesic Drugs
6.
Clinical Evaluation of Antacid Drugs
7.
Clinical Evaluation of Anti-Inflammatory and Antirheumatic Drugs (adults and children)
(Issued 9/19/1994, Posted 3/2/1998)
(Issued 12/1992, Posted 3/2/1998)
(Issued, 4/1/78, Posted 3/2/1998)
8.
Clinical Evaluation of Antianxiety Drugs
9.
Clinical Evaluation of Antidepressant Drugs
10. Clinical Evaluation of Antidiarrheal Drugs
11. Clinical Evaluation of Antiepileptic Drugs (adults and children)
12. Clinical Evaluation of Gastric Secretory Depressant (GSD) Drugs
13. Clinical Evaluation of General Anesthetics
(Issued 1/1981, Posted 3/2/1998)
(Issued 5/1982, Posted 3/2/1998)
14. Clinical Evaluation of Hypnotic Drugs
15. Clinical Evaluation of Laxative Drugs
16. Clinical Evaluation of Local Anesthetics
(Posted 3/2/1998)
17. Clinical Evaluation of Psychoactive Drugs in Infants and Children
18. Clinical Evaluation of Radiopharmaceutical Drugs
(Posted 3/2/1998)
(Posted 3/2/1998)
19. Content and Format for Pediatric Use Supplements
20. Content and Format of Investigational New Drug Applications (INDs) for Phase 1 Studies of Drugs, Including
Well-Characterized, Therapeutic, Biotechnology-derived Products
21. Establishing Pregnancy Exposure Registries [Word] or [PDF] (Issued 8/2002, Posted 9/20/2002)
22. FDA Approval of New Cancer Treatment Uses for Marketed Drug and Biological Products [HTML] or [PDF]
(Issued 12/1998, Posted 2/2/1999, HTML posted 9/14/1999)
23. FDA Requirements for Approval of Drugs to Treat Non-Small Cell Lung Cancer
24. FDA Requirements for Approval of Drugs to Treat Superficial Bladder Cancer
25. Format and Content of the Clinical and Statistical Sections of an Application
5/21/1997)
(Posted 3/2/1998)
(Posted 3/2/1998)
(Issued 7/1988, Posted
26. Format and Content of the Summary for New Drug and Antibiotic Applications*
3/2/1998)
27. Formatting, Assembling and Submitting New Drug and Antibiotic Applications*
3/2/1998)
28. General Considerations for the Clinical Evaluation of Drugs
29. General Considerations for the Clinical Evaluation of Drugs in Infants and Children
3/2/1998)
(Posted 3/2/1998)
30. Guidance for the Development of Vaginal Contraceptive Drugs (NDA)
31. Levothyroxine Sodium Tablets - In Vivo Pharmacokinetic and Bioavailability Studies and In Vitro Dissolution
Testing [HTML] or [PDF] (Issued 2/2001, Posted 3/8/2001)
32. Oncologic Drugs Advisory Committee Discussion on FDA Requirements for Approval of New Drugs for
Treatment of Ovarian Cancer
(Posted 3/2/1998)
33. Oncologic Drugs Advisory Committee Discussion on FDA Requirements or Approval of New Drugs for
Treatment of Colon and Rectal Cancer
(Posted 3/2/1998)
34. Postmarketing Adverse Experience Reporting for Human Drug and Licensed Biological Products: Clarification
of What to Report
or WordPerfect 6.x version (Issued 8/27/1997, Posted 8/27/1997)
35. Postmarketing Reporting of Adverse Drug Experiences
(Issued 3/1992, Posted 3/2/1998)
36. Preclinical Development of Immunomodulatory Drugs for Treatment of HIV Infection and Associated Disorders
(Reposted 1/21/1999)
37. Preparation of Investigational New Drug Products (Human and Animal)
(Issued 11/1992, Posted 3/2/1998)
38. Providing Clinical Evidence of Effectiveness for Human Drug and Biological Products
(Issued 5/14/1998,
Posted 5/14/1998)
39. Prussian Blue Drug Products — Submitting a New Drug Application [Word] [PDF] (Issued 1/2003, Posted
2/4/2003)
40. Study and Evaluation of Gender Differences in the Clinical Evaluation of Drugs
3/2/1998)
41. Study of Drugs Likely to be used in the Elderly
(Issued 11/1989, Posted 3/2/1998)
42. Submission of Abbreviated Reports and Synopses in Support of Marketing Applications [HTML] or [PDF]
(Issued 8/1999, Posted 9/13/1999)
٧ Clinical/Medical (Draft)
1.
2.
3.
4.
5.
6.
7.
Allergic Rhinitis: Clinical Development Programs for Drug Products [HTML] or [PDF] (Issued 6/2000, Posted
6/20/2000)
Available Therapy [HTML] or [PDF] (Posted 2/6/2002)
Chronic Cutaneous Ulcer and Burn Wounds — Developing Products for Treatment [HTML] or [PDF] (Issued
6/2000, Posted 6/27/2000)
Clinical Development Programs for Drugs, Devices, and Biological Products Intended for the Treatment of
Osteoarthritis [Word] or [PDF] (Issued 7/07/1999, Posted 7/14/1999)
Clinical Evaluation of Lipid-Altering Agents (Issued 10/1990, Posted 2/18/1998)
Clinical Evaluation of Weight-Control Drugs (9/24/1996, Posted 2/18/1998)
Clinical Trial Sponsors On the Establishment and Operation of Clinical Trial Dada Monitoring Committees
[HTML] or [PDF] (11/15/2001)
8. Developing Medical Imaging Drugs and Biologics [HTML] or [PDF] (Issued 7/2000, Posted 7/28/2000)
9. Development of Parathyroid Hormone for the Prevention and Treatment of Osteoporosis [HTML] or [PDF]
(Issued 5/2000, Posted 6/13/2000)
10. Drugs, Biologics, and Medical Devices Derived from Bioengineered Plants for Use in Humans and Animals
[HTML]or [PDF] (Issued 9/6/2002)
11. Estrogen and Estrogen/Progestin Drug Products to Treat Vasomotor Symptoms and Vulvar and Vaginal
Atrophy Symptoms — Recommendations for Clinical Evaluation [Word] or [PDF] (Issued 1/2003, Posted
1/30/2003)
12. Evaluation of Human Pregnancy Outcome Data [HTML] or [PDF] (Issued 6/2/1999, Posted 6/8/99)
13. Evaluation of the Effects of Orally Inhaled and Intranasal Corticosteroids on Growth in Children [HTML] or [PDF]
(Posted 11/6/2001)
14. Exercise-Induced Bronchospasm (EIB) — Development of Drugs to Prevent EIB [PDF] (Issued 2/2002, Posted
2/19/2002)
15. Female Sexual Dysfunction: Clinical Development of Drug Products for Treatment [HTML] or [PDF] (Issued
5/2000, Posted 5/18/2000)
16. Guidance for Institutional Review Boards, Clinical Investigators, and Sponsors: Exception from Informed
Consent Requirements for Emergency Research (3/31/2000)
17. IND Exemptions for Studies of Lawfully Marketed Cancer Drug or Biological Products [PDF] (Issued 4/2002,
Posted 4/9/2002)
18. Inhalation Drug Products Packaged in Semipermeable Container Closure Systems [PDF] (Issued 7/2002,
Posted 7/25/2002)
19. OTC Treatment of Herpes Labialis with Antiviral Agents [HTML] or [PDF] (Issued 3/8/2000, Posted 3/8/2000)
20. Pediatric Oncology Studies In Response to a Written Request [HTML] or [PDF] (Issued 6/2000, Posted
6/19/2000)
21. Preclinical and Clinical Evaluation of Agents Used in the Prevention or Treatment of Postmenopausal
Osteoporosis (Issued 4/1994, Posted 2/18/1998)
22. Recommendations for Complying with the Pediatric Rule (21 CFR 314.55(a) and 601.27(a)) [HTML] or [PDF]
(Posted 12/1/2000)
Clinical Pharmacology
1.
Drug Metabolism/Drug Interaction Studies in the Drug Development Process: Studies In Vitro
4/1997, Posted 4/8/1997)
2.
Format and Content of the Human Pharmacokinetics and Bioavailability Section of an Application*
(Issued
2/1987, Posted 3/2/1998)
In Vivo Drug Metabolism/Drug Interaction Studies - Study Design, Data Analysis, and Recommendations for
Dosing and Labeling [HTML] or [PDF] (Issued 11/24/1999, Posted 11/24/1999)
3.
4.
Pharmacokinetics in Patients with Impaired Renal Function
5.
Population Pharmacokinetics
(Issued
(Issued 5/14/1998, Posted 5/14/1998)
(Issued 2/1999, Posted 2/10/1999)
Clinical Pharmacology (Draft)
1.
Exposure-Response Relationships: Study Design, Data Analysis, and Regulatory Applications [PDF] (Issued
4/1/2002, Posted 4/1/2002)
2.
(Issued
General Considerations for Pediatric Pharmacokinetic Studies for Drugs and Biological Products
11/1998, Posted 11/12/1998)
Pharmacokinetics in Patients With Impaired Hepatic Function: Study Design, Data Analysis, and Impact on
Dosing and Labeling [HTML] or [PDF] (Issued 11/1999, Posted 12/6/1999)
3.
Compliance
1.
2.
3.
4.
5.
6.
7.
A Review of FDA's Implementation of the Drug Export Amendments of 1986
3/2/1998)
Compressed Medical Gases (Issued 2/1989, Posted 3/10/1997)
Computerized Systems Used in Clinical Trials [HTML] - [Acrobat Version] (Issued 4/1999, Posted 5/11/1999)
General Principles of Process Validation
(Posted 3/2/1998)
Good Laboratory Practice Regulations Questions and Answers
Guidance for Hospitals, Nursing Homes, and Other Health Care Facilities - FDA Public Health Advisory [HTML]
or [PDF] (Issued and Posted 4/5/2001)
Guideline for Validation of Limulus Amebocyte Lysate Test as an End-Product Endotoxin Test for Human and
Animal Parenteral Drugs, Biological Products, and Medical Devices
(Posted 3/2/1998)
8.
Expiration Dating and Stability Testing of Solid Oral Dosage Form Drugs Containing Iron
Posted 6/27/1997)
9.
Monitoring of Clinical Investigations
(Issued 6/27/1997,
(Posted 3/2/1998)
10. Nuclear Pharmacy Guideline Criteria for Determining When to Register as a Drug Establishment
3/2/1998)
(Posted
11. Possible Dioxin/PCB Contamination of Drug and Biological Products [HTML] or [PDF] (Issued 8/23/1999,
Posted 8/23/1999)
12. Street Drug Alternatives [HTML] or [PDF] (Issued 3/2000, Posted 3/31/2000)
13. Sterile Drug Products Produced by Aseptic Processing
(Issued 6/1987, Posted 3/2/1998)
Compliance (Draft)
1.
2.
3.
4.
5.
6.
Guidance for IRBs, Clinical Investigators, and Sponsors: Exception from Informed Consent Requirements for
Emergency Research (21 CFR 50.24) Draft released for comment 3/30/2000 (5/12/2000)
Investigating Out of Specification (OOS) Test Results for Pharmaceutical Production (Issued 9/30/1998, Posted
9/30/1998)
Manufacturing, Processing, or Holding Active Pharmaceutical Ingredients
4/17/1998)
PET Drug Products - Current Good Manufacturing Practice (CGMP) [HTML] or [PDF] (Issued 3/29/2002,
Posted 3/29/2002)
Part 11, Electronic Records; Electronic Signatures -- Scope and Application [Word] or [PDF] (Issued 2/2003,
Posted 2/20/2003)
Prescription Drug Marketing Act Regulations for Donation of Prescription Drug Samples to Free Clinics [HTML]
or [PDF] (Issued 6/2002, Posted 6/27/2002)
Electronic Submissions
1.
Providing Regulatory Submissions in Electronic Format — ANDAs [HTML] or [PDF] (Issued 6/2002, Posted
6/27/2002)
2.
Regulatory Submissions in Electronic Format; General Considerations
3.
Regulatory Submissions in Electronic Format; New Drug Applications
(Issued 1/1999, Posted 1/27/1999)
(Issued 1/1999, Posted 1/27/1999)
Example of an Electronic New Drug Application Submission (Posted 2/17/1999).
Electronic Submissions Draft
1.
2.
3.
Part 11, Electronic Records; Electronic Signatures -- Scope and Application [Word] or [PDF] (Issued 2/2003,
Posted 2/20/2003)
Providing Regulatory Submissions in Electronic Format - Postmarketing Expedited Safety Reports [HTML] or
Providing Regulatory Submissions in Electronic Format - Prescription Drug Advertising and Promotional
Labeling [HTML] or [PDF] (Issued 1/2001, Posted 1/30/2001)
Generics
1.
2.
3.
4.
Alternate Source of the Active Pharmaceutical Ingredient in Pending ANDAs [HTML] or [PDF] (Posted
12/12/2000)
ANDA's: Impurities in Drug Substances [HTML] or [PDF] (Issued 11/1999, Posted 12/2/1999)
Court Decisions, ANDA Approvals, and 180-Day Exclusivity Under the Hatch-Waxman Amendments to the
Federal Food, Drug, and Cosmetic Act [HTML] or [PDF] (Posted 3/27/2000)
Letter announcing that the OGD will now accept the ICH long-term storage conditions as well as the stability
5.
studies conducted in the past.
(Posted 3/2/1998)
Letter describing efforts by the CDER and the ORA to clarify the responsibilities of CDER chemistry review
scientists and ORA field investigators in the new and abbreviated drug approval process in order to reduce
6.
duplication or redundancy in the process
(Posted 3/2/1998)
Letter on incomplete Abbreviated Applications, Convictions Under GDEA, Multiple Supplements, Annual
Reports for Bulk Antibiotics, Batch Size for Transdermal Drugs, Bioequivalence Protocols, Research,
7.
Deviations from OGD Policy
(Posted 3/2/1998)
Letter on the Provision of new information pertaining to new bioequivalence guidelines and refuse-to-file letters
(Posted 3/2/1998)
8.
Letter on the provision of new procedures and policies affecting the generic drug review process
3/2/1998)
(Posted
9.
Letter on the request for cooperation of regulated industry to improve the efficiency and effectiveness of the
generic drug review process, by assuring the completeness and accuracy of required information and data
submissions
(Posted 3/2/1998)
10. Letter on the response to 12/20/1984 letter from the Pharmaceutical Manufacturers Association about the Drug
Price Competition and Patent Term Restoration Act
(Posted 3/2/1998)
11. Letter to all ANDA and AADA applicants about the Generic Drug Enforcement Act of 1992 (GDEA), and the
Office of Generic Drugs intention to refuse-to-file incomplete submissions as required by the new law
(Posted 3/2/1998)
12. Letter to regulated industry notifying interested parties about important detailed information regarding labeling,
scale-up, packaging, minor/major amendment criteria and bioequivalence requirements
(Posted 3/2/1998)
13. Major, Minor, and Telephone Amendments to Abbreviated New Drug Applications [PDF] (Issued 12/2001,
Posted 12/20/2001)
14. Organization of an ANDA
(Issued 2/1999, 3/2/1999)
15. Revising ANDA Labeling Following Revision of the RLD Labeling [HTML] or [PDF] (Issued 4/26/2000,
4/26/2000)
16. Skin Irritation and Sensitization Testing of Generic Transdermal Drug Products [HTML] or [PDF] (Issued
12/1999, Posted 2/3/2000)
17. Variations in Drug Products that May Be Included in a Single ANDA
(Issued 12/1998, Posted 1/26/1999)
Generics (Draft)
1.
2.
ANDAs: Impurities in Drug Products
(Issued 12/1998, Posted 1/5/1999)
Content and Format of an Abbreviated New Drug Application (ANDA) - Positron Emission Tomography (PET)
Drug Products With specific information for ANDAs for Fludeoxyglucose F18 Injection (Issued 4/1997, Posted
3.
4.
Handling and Retention of BA and BE Testing Samples
(Issued 8/2002, Posted 8/20/2002)
Potassium Chloride Modified-Release Tablets and Capsules: In Vivo Bioequivalence and In Vitro Dissolution
Testing [PDF] (Issued 8/2002, Posted 8/6/2002)
4/23/1997)
Good Review Practices (GRPs)
1.
Pharmacology/Toxicology Review Format [PDF] (Posted 5/9/2001)
Good Review Practices (GRPs) (Draft)
1.
Conducting a Clinical Safety Review of a New Product Application and Preparing a Report on the Review
(11/1996)
Industry Letters
1.
Continuation of a series of letters communicating interim and informal generic drug policy and guidance.
Availability of Policy and Procedure Guides, and further operational changes to the generic drug review
2.
program
(Posted 3/2/1998)
Fifth of a series of letters providing informal notice about the Act, discussing the statutory mechanism by which
3.
ANDA applicants may make modifications in approved drugs where clinical data is required
(Posted
3/2/1998)
Fourth of a series of letters providing informal notice to all affected parties about policy developments and
interpretations regarding the Act. Three year exclusivity provisions of Title I
(Posted 3/2/1998)
4.
Implementation of the Drug Price Competition and Patent Term Restoration Act. Preliminary Guidance
(Posted 3/2/1998)
5.
6.
Implementation Plan USP injection nomenclature
(Posted 3/2/1998)
Seventh of a series of letters about the Act providing guidance on the "130-day exclusivity" provision of section
7.
505(j)(4)(B)(iv) of the FD&C
(Posted 3/2/1998)
Sixth of a series of informal notice letters about the Act discussing 3-and 5-year exclusivity provisions of
sections 505(c)(3)(D) and 505(j)(4)(D) of the FD&C Act
8.
(Posted 3/2/1998)
Supplement to 10/11/1984 letter about policies, procedures and implementation of the Act (Q&A format)
(Posted 3/2/1998)
9. Third of a series of letters regarding the implementation of the Act
10. Year 2000 Letter from Dr. Janet Woodcock (10/19/98)
(Posted 3/2/1998)
International Conference on Harmonisation
Safety
1.
S1A The Need for Long-term Rodent Carcinogenicity Studies of Pharmaceuticals
2.
S1B Testing for Carcinogenicity of Pharmaceuticals
3.
4.
S1C Dose Selection for Carcinogenicity Studies of Pharmaceuticals
S1C(R) Guidance on Dose Selection for Carcinogenicity Studies of Pharmaceuticals: Addendum on a Limit
5.
S2A Specific Aspects of Regulatory Genotoxicity Tests for Pharmaceuticals
6.
S2B Genotoxicity: A Standard Battery for Genotoxicity Testing of Pharmaceuticals
Posted 5/4/1998)
Dose and Related Notes
(Issued 2/28/1998, Posted 3/24/1998)
(Issued 12/4/1997, Posted 12/11/1997)
(Issued 11/21/1997,
7.
S3A Toxicokinetics: The Assessment of Systemic Exposure in Toxicity Studies
8.
9.
S3B Pharmacokinetics: Guidance for Repeated Dose Tissue Distribution Studies
S4A Duration of Chronic Toxicity Testing in Animals (Rodent and Nonrodent Toxicity Testing) [PDF] or [Text]
Posted 6/25/99
10. S5A Detection of Toxicity to Reproduction for Medicinal Products
(Issued 9/1994, Posted 4/23/1997)
11. S5B Detection of Toxicity to Reproduction for Medicinal Products: Addendum on Toxicity to Male Fertility
12. S6 Preclinical Safety Evaluation of Biotechnology-Derived Pharmaceuticals
11/18/1997)
13. S7A Safety Pharmacology Studies for Human Pharmaceuticals [HTML] or [PDF] (Issued 7/2001, Posted
7/12/2001)
Joint Safety/Efficacy (Multidisciplinary)
1.
M2 eCTD: Electronic Common Technical Document Specification [PDF] (Posted 4/1/2003)
2.
M3 Nonclinical Safety Studies for the Conduct of Human Clinical Trials for Pharmaceuticals
(Issued 11/1997,
Posted 11/25/1997)
M4: Common Technical Document for the Registration of Pharmaceuticals for Human Use (Posted 10/15/2001)
o M4: Organization of the CTD [HTML] or [PDF]
M4: The CTD -- General Questions and Answers [WORD] or [PDF] (Issued 2/2003, Posted
2/3/2003)
o M4: The CTD -- Quality [HTML] or [PDF]
o M4: The CTD -- Efficacy [HTML] or [PDF]
M4: The CTD -- Efficacy Questions and Answers [WORD] or [PDF] (Issued 1/2003, Posted
2/4/2003)
o M4: The CTD -- Safety [HTML] or [PDF]
o M4: The CTD -- Safety Appendices [HTML] or [WORD] or [PDF]
M4: The CTD -- Safety Questions and Answers [WORD] or [PDF] (Issued 2/2003, Posted
2/4/2003)
3.
Efficacy
1.
E1A The Extent of Population Exposure to Assess Clinical Safety: For Drugs Intended for Long-term Treatment
2.
3.
E2A Clinical Safety Data Management: Definitions and Standards for Expedited Reporting
E2B International Conference on Harmonisation; Guidance on Data Elements for Transmission of Individual
of Non-Life-Threatening Conditions
Case Safety Reports (Issued 1/15/1998, Posted 1/15/1998)
o
E2BM Data Elements for Transmission Of Individual Case Safety Reports
4/4/2002)
4.
E2C Clinical Safety Data Management: Periodic Safety Update Reports for Marketed Drugs
5/19/1997, Posted 3/19/1998)
5.
E3 Structure and Content of Clinical Study Reports
6.
E4 Dose-Response Information to Support Drug Registration
7.
E5 Ethnic Factors in the Acceptability of Foreign Clinical Data
8.
E6 Good Clinical Practice: Consolidated Guideline
(Issued
(Issued 6/10/1998, Posted 6/10/1998)
Spanish Version (Issued 5/9/1997, Posted 3/19/1998)
9.
E7 Studies in Support of Special Populations: Geriatrics
10. E8 General Considerations for Clinical Trials
(Issued 12/1997, Posted 12/17/1997)
(9/1/1998)
11. E9 Statistical Principles for Clinical Trials
12. E 10 Choice of Control Group and Related Issues in Clinical Trials [HTML] or [PDF] (Issued 5/2001, Posted
5/11/2001)
13. E11 Clinical Investigation of Medicinal Products in the Pediatric Population [Acrobat] (Issued 12/2000, Posted
12/14/2000)
Quality
1.
2.
Q1A(R) Stability Testing of New Drug Substances and Products [HTML] or [PDF] (Posted 11/6/2001)
Q1B Photostability Testing of New Drug Substances and Products [HTML] or [PDF] (Issued 11/1996, Reposted
7/7/1998)
3.
4.
Q1C Stability Testing for New Dosage Forms
(Issued 5/9/1997, Posted 3/19/1998)
Q1D Bracketing and Matrixing Designs for Stability Testing of New Drug Substances and Products [Word] or
5.
Q2A Text on Validation of Analytical Procedures
6.
7.
Q2B Validation of Analytical Procedures: Methodology
(Issued 5/19/1997, Posted 3/19/1997)
Q3A Impurities in New Drug Substances [Word] or [PDF] (Issued 2/10/2003, Posted 2/10/2003)
8.
Q3B Impurities in New Drug Products or Adobe Acrobat version
9.
Q3C Impurities: Residual Solvents or Adobe Acrobat version
(Issued 5/19/1997, Reposted 7/7/1998)
(Issued 12/24/1997, Posted 12/30/1997)
Q3C Tables and List or Adobe Acrobat version
(Posted 10/2/2001).
Appendix 4, Appendix 5, and Appendix 6 (Appendices were issued with the Q3C draft guidance documents)
Maintenance Procedures for Updating (Posted 2/11/2002)
10. Q5A Viral Safety Evaluation of Biotechnology Products Derived From Cell Lines of Human or Animal Origin
(Posted 9/1998)
11. Q5B Quality of Biotechnological Products: Analysis of the Expression Construct in Cells Used for Production of
r-DNA Derived Protein Products
12. Q5C Quality of Biotechnological Products: Stability Testing of Biotechnological/Biological Products
13. Q5D Quality of Biotechnological/Biological Products: Derivation and Characterization of Cell Substrates Used
for Production of Biotechnological/Biological Products; Availability
(Issued 9/21/1998, Posted 9/21/1998)
14. Q6A International Conference on Harmonisation; Guidance on Q6A Specifications: Test Procedures and
Acceptance Criteria for New Drug Substances and New Drug Products: Chemical Substances. [Text] or [PDF]
(12/29/2000)
15. Q6B Specifications: Test Procedures and Acceptance Criteria for Biotechnological/Biological Products [PDF]
(Issued 8/1999, Posted 12/14/2001)
16. Q7A Good Manufacturing Practice Guidance for Active Pharmaceutical Ingredients [HTML] or [PDF] (Issued
8/2001, Posted 9/24/2001]
International Conference on Harmonisation (Draft)
Efficacy
1.
E2C Addendum to ICH E2C Clinical Safety Data Management Periodic Safety Update Reports for Marketed
2.
Drugs
(Issued 9/2002, Posted 12/27/2002)
Principles for Clinical Evaluation of New Antihypertensive Drugs. Optional Format: PDF. (Issued 8/2000,
Posted 8/8/2000)
Joint Safety/Efficacy (Multidisciplinary) (Draft)
1.
2.
3.
M2 Electronic Common Technical Document Specification [HTML] or [PDF] (Issued 6/2002, Posted 6/13/2002)
Submitting Marketing Applications According to the ICH/CTD Format: General Considerations (Issued 9/2001,
Posted 9/5/2001) [PDF]
M4 Common Technical Document--Quality: Questions and Answers/Location Issues [HTML] or [PDF] or
[WORD] (Issued 9/12/2002, Posted 12/30/2002)
Quality
1.
2.
3.
Q3B(R) Impurities in New Drug Products [Text] or [PDF] (Issued 7/2000, Posted 7/19/2000)
Q1E Evaluation of Stability Data [HTML] or [PDF] (Issued 6/2002, Posted 6/13/2002)
Q1F Stability Data Package for Registration in Climatic Zones III and IV [HTML] or [PDF] (Issued 6/2002,
Posted 6/13/2002)
Safety
1.
S7B Safety Pharmacology Studies for Assessing the Potential for Delayed Ventricular Repolarization (QT
Interval Prolongation) by Human Pharmaceuticals [HTML] or [PDF] (Issued 6/2002, Posted 6/13/2002)
Investigational New Drug Applications
1.
Content and Format of Investigational New Drug Applications (INDs) for Phase 1 Studies of Drugs
Labeling
The unlinked guidance's below have been withdrawn. See Federal Register Notice from July 5, 2002 : [Text] or
[PDF]
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
47.
Acetaminophen, Aspirin and Codeine Phosphate Tablets and Acetaminophen, Aspirin and Codeine Phosphate
Capsules
Acetaminophen and codeine Phosphate Oral Solution and oral suspension (Issued 12/1993, Posted
7/11/1997)
Alprazolam Tablets or WordPerfect 6.x Version (Issued 8/1996, Posted 8/21/1997)
Amiloride Hydrochloride and Hydrochlorothiazide Tablets USP (Issued 9/1997, Posted 10/16/1997)
Amlodipine Besylate Tablets (Issued 9/1997, Posted 10/15/1997)
Astemizole Tablets (Issued 9/1997, Posted 10/15/1997)
Atenolol Tablets or WordPerfect 6.x Version (Issued 8/1997, Posted 8/21/1997)
Butalbital, Acetaminophen and Caffeine Tablets USP, Butalbital, Acetaminophen and Caffeine Capsules USP
(Issued 9/1997, Posted 10/16/1997)
Butalbital, Acetaminophen, Caffeine and Hydrocodone Bitartrate Tablets (Issued 9/1997, Posted 10/16/1997)
Butorphanol Tartrate Injection USP
Captopril and Hydrochlorothiazide Tablets, USP
Captopril Tablets (Issued 2/1995, Posted 7/11/1997)
Carbidopa and Levodopa Tablets USP
Cimetidine Hydrochloride Injection (Issued 9/1995, Posted 7/11/1997)
Cimetidine Tablets, USP
Cisapride Oral Suspension (Issued 9/1997, Posted 10/16/1997)
Cisapride Tablets (Issued 9/1997, Posted 10/20/1997)
Clindamycin Phosphate Injection USP (Revised 9/1998, Posted 10/15/1998)
Content and Format for Geriatric Labeling [HTML] or [PDF] (Issued 10/2001, Posted 10/4/2001)
Diclofenac Sodium Delayed-release Tablets
Diltiazem Hydrochloride Extended-release Capsules
Diphenoxylate Hydrochloride and Atropine Sulfate Oral Solution, USP
Diphenoxylate Hydrochloride and Atropine Sulfate Tablets, USP
Fludeoxyglucose F18 Injection (Issued 1/1997, Posted 3/1/1997)
Flurbiprofen Tablets USP
Fluvoxamine Maleate Tablets (Issued 9/1997, Posted 10/20/1997)
Gentamicin Sulfate Ophthalmic Solution USP and Gentamicin Sulfate Ophthalmic Ointment USP
Heparin Sodium Injection, USP
Hydrocodone Bitartrate and Acetaminophen Tablets USP
Indomethacin Capsules, USP
Itraconazole Capsules (Isssued 9/1998, Posted 10/15/1998)
Leucovorin Calcium for Injection (Issued 7/1996, Posted 7/11/1997)
Leucovorin Calcium Tablets USP (Issued 7/1996, Posted 7/11/1996)
Medroxyprogesterone Acetate Tablets, USP (Revised 9/1998, Posted 10/15/1998)
Metaproternol Sulfate Inhalation Solution USP
Metaproterenol Sulfate Syrup, USP
Metaproterenol Sulfate Tablets, USP
Metoclopramide Tablets USP and Metoclopramide Oral Solution USP
Naproxen Sodium Tablets USP (Issued 9/1997, Posted 10/16/1997)
Naproxen Tablets USP (Issued 9/1997, Posted 10/16/1997)
Paclitaxel Injection (Issued 9/1997, Posted 10/15/1997)
Quinidine Sulfate Tablets USP (Issued 10/1995, Posted 7/11/1997)
Ranitidine Tablets USP
Risperidone Oral Solution (Issued 9/1997, Posted 10/15/1997)
Risperidone Tablets (Issued 9/1997, Posted 10/15/1997)
Sulfacetamide Sodium Ophthalmic Solution USP and Sulfacetamide Sodium Ophthalmic Ointment USP
Sulfacetamide Sodium and Prednisolone Acetate
48. Sulfamethoxazole and Trimethoprim Tablets USP and Sulfamethoxazole and Trimethoprim Oral Suspension
USP
49. Theophylline
50. Theophylline Intravenous Dosage Forms (Issued 9/1/1995, Posted 7/11/1997)
51. Tobramycin Sulfate Injection USP
52. Venlafaxine Hydrochloride Tablets (Issued 10/1997, Posted 12/23/1997)
53. Verapamil Hydrochloride Tablets
54. Zolpidem Tartrate Tablets (Issued 9/1997, Posted 10/15/1997)
Labeling (Draft)
1.
2.
3.
4.
5.
6.
Clinical Studies Section of Labeling for Prescription Drugs and Biologics-- Content and Format [HTML] or [PDF]
(Issued 7/2001, Posted 7/9/2001)
Combined Oral Contraceptives - Labeling for Healthcare Providers and Patients [HTML] or [PDF] (Issued
7/2000, Posted 7/7/2000)
Content and Format of the Adverse Reactions Section of Labeling for Human Prescription Drugs and Biologics
[HTML] or [PDF] (Issued 6/2000, Posted 6/20/2000)
Draft Guidance for Industry on Labeling of OTC Topical Drug Products for the Treatment of Vaginal Yeast
Infections (Vulvovaginal Candidiasis)
(Issued 6/1998, Posted 7/20/98)
Labeling Guidance for Noncontraceptive Estrogen Drug Products for the Treatment of Vasomotor Symptoms
and Vulvar and Vaginal Atrophy Symptoms — Prescribing Information for Health Care Providers and Patient
Labeling [Word] or [PDF] (Posted 1/31/2003)
Referencing Discontinued Labeling for Listed Drugs in Abbreviated New Drug Applications [HTML] or [PDF]
(Issued 10/2000, Posted 10/25/2000)
Microbiology
1.
Format and Content of the Microbiology Section of an Application*
Modernization Act of 1997
1.
Changes to an Approved NDA or ANDA [HTML] or [PDF] (Issued 11/1999, Posted 11/19/1999)
2.
Classifying Resubmissions in Response to Action Letters
3.
Enforcement Policy During Implementation of Section 503A of the Federal Food, Drug, and Cosmetic Act
Word Version (Issued 11/1998, Posted 11/20/1998)
4.
Fast Track Drug Development Programs – Designation, Development, and Application Review
5.
6.
7.
8.
9.
(Issued 5/14/1998, Posted 5/14/1998)
or
; Appendix 3
consisting of Mapp 6020.3 and SOPP 8405;and Appendix 4
[Appendices are
Appendix 2
scanned copies, which will be replaced by final versions 11/18] (Issued 11/17/1998, Posted 11/17/1998)
Formal Dispute Resolution: Appeals Above the Division Level [HTML] or [PDF] (Issued 2/2000, Posted
3/6/2000)
Formal Meetings With Sponsors and Applicants for PDUFA Products [HTML] or [PDF] (Issued 2/2000, Posted
3/6/2000)
Implementation of Section 120 of the Food and Drug Administration Modernization Act of 1997-Advisory
Committees Wordperfect or Acrobat Version (Issued 10/1998, Posted 11/02/98)
Implementation of Section 126 of the Food and Drug Administration Modernization Act of 1997 - Elimination of
Certain Labeling Requirements
(Issued 7/1998, Posted 7/20/98)
Information Program on Clinical Trials for Serious or Life-Threatening Diseases and Conditions [HTML] or
10. National Uniformity for Nonpresciption Drugs - Ingredient Listing for OTC Drugs
5/5/1998)
(Issued 5/14/1998,
11. Providing Clinical Evidence of Effectiveness for Human Drug and Biological Products
Posted 5/14/1998)
12. Qualifying for Pediatric Exclusivity Under Section 505A of the Federal Food, Drug, and Cosmetic Act [HTML] or
o Frequently Asked Questions on Pediatric Exclusivity (505A), The Pediatric "Rule," and Their
Interaction (Posted 7/27/1999)
13. Repeal of Section 507 of the Federal Food, Drug and Cosmetic Act
(Revised 5/1998, Posted 6/12/1998)
14. Standards for Prompt Review of Efficacy Supplements
(Issued 5/15/1998, Posted 5/15/1998)
15. Submission of Abbreviated Reports and Synopses in Support of Marketing Applications (Issued 8/1998, Posted
9/15/98)
16. Submitting and Reviewing Complete Responses to Clinical Holds (Revised) [HTML] or [PDF] (Issued 10/2000,
Posted 10/25/2000
17. Women and Minorities Guidance Requirements
(Issued 7/20/1998, Posted 11/25/1998)
Modernization Act of 1997 (Draft)
1.
2.
3.
Information Program on Clinical Trials for Serious or Life-Threatening Diseases: Establishment of a Data Bank
[HTML] or PDF (Issued 3/2000, Posted 3/28/2000)
PET Drug Applications - Content and Format for NDAs and ANDAs [HTML] or [PDF] (Issued 3/7/2000, Posted
3/7/2000)
o Sample formats for chemistry, manufacturing, and controls sections [PDF] or [Word97]
o Sample formats for labeling [PDF] or [Word97]
o Sample formats for Form FDA 356h [PDF] or [Word97]
o Sample formats for user fee Form FDA 3397 [PDF] or [Word97]
Reports on the Status of Postmarketing Studies - Implementation of Section 130 of the Food and Drug
Administration Modernization Act of 1997 [HTML] or [PDF] (Posted 4/4/2001)
Over-the-Counter (OTC) Guidances
1.
Enforcement Policy on Marketing OTC Combination Products (CPG 7132b.16)
2.
3.
(Posted 3/2/1998)
General Guidelines for OTC Combination Products
Labeling OTC Human Drug Products Using a Column Format [HTML] or [PDF] (Issued 12/2000, Posted
12/18/2000)
Labeling OTC Human Drug Products Updating Labeling in RLDs and ANDAs [Word] or [PDF]
Example Drug Facts Labels
o Acetaminophen 120 mg in a Suppository Dosage Form [PDF]
o Cimetidine 200 mg in a Tablet Dosage Form [PDF]
o Clemastine Fumerate 1.34 mg in a Tablet Dosage Form [PDF]
o Doxylamine Succinate 25 mg Tablet Dosage Form [PDF]
o Ibuprofen 200 mg in a Tablet/Capsule Dosage Form [PDF]
o Loperamide HCl in a Liquid Dosage Form [PDF]
o Loperamide HCl in a Tablet/Caplet Dosage Form [PDF]
o Miconazole Nitrate Vaginal Products [PDF]
o Minoxidil Topical Solution 2% for Men and Women [PDF]
o Minoxidil Topical Solution 5% for Men [PDF]
o Naproxen Sodium 220 mg in a Tablet/Caplet/Gelcap Dosage Form [PDF]
o Pseudoephedrine HCl Extended-Release Tablets 120 mg [PDF]
4.
5.
Upgrading Category III Antiperspirants to Category I (43 FR 46728-46731)
(Posted 3/2/1998)
(Posted 3/2/1998)
Over-the-Counter (OTC) Draft
1.
2.
Labeling OTC Human Drug Products -Submitting Requests for Exemptions and Deferrals [HTML] or [PDF]
(Issued 12/2000, Posted 12/18/2000)
Labeling OTC Human Drug Products Updating Labeling in ANDAs [HTML] or [PDF] (2/21/2001)
o
o
o
Additional examples 1
(3/19/2001)
(3/26/2001)
(3/26/2001)
Pharmacology/Toxicology
1.
2.
Carcinogenicity Study Protocol Submissions [HTML] or [PDF] (Issued 5/22/2002)
Content and Format of INDs for Phase 1 Studies of Drugs, Including Well-Characterized, Therapeutic,
Biotechnology-Derived Products [HTML] or [PDF]
3.
Format and Content of the Nonclinical Pharmacology/Toxicology Section of an Application*
(Posted
3/2/1998)
Immunotoxicology Evaluation of Investigational New Drugs [Word] or [PDF] (Issued 10/2002, Posted
10/31/2002)
4.
5.
6.
Nonclinical Pharmacology/Toxicology Development of Topical Drugs Intended to Prevent the Transmission of
Sexually Transmitted Diseases (STD) and/or for the Development of Drugs Intended to Act as Vaginal
Contraceptives
Reference Guide for the Nonclinical Toxicity Studies of Antivial Drugs Indicated for the Treatment of N/A Non-
7.
Single Dose Acute Toxicity Testing for Pharmaceuticals
Life Threatening Disease Evaluation of Drug Toxicity Prior to Phase I Clinical Studies
(Posted 3/2/1998)
Pharmacology/Toxicology Draft
1.
2.
3.
4.
5.
6.
Estimating the Safe Starting Dose in Clinical Trials for Therapeutics in Adult Healthy Volunteers [Word] or [PDF]
(Issued 1/2003, Posted 1/15/2003)
Integration of Study Results to Assess Concerns about Human Reproductive and Developmental Toxicities
Nonclinical Safety Evaluation of Pediatric Drug Products [Word] or [PDF] (Issued 1/2003, Posted 1/31/2003)
Nonclinical Studies for Development of Pharmaceutical Excipients [Word] or [PDF] (Issued 10/2002, Posted
10/2/2002)
Photosafety Testing [HTML] or [PDF] (Issued 1/2000, Posted 1/7/2000)
Statistical Aspects of the Design, Analysis, and Interpretation of Chronic Rodent Carcinogenicity Studies of
Pharmaceuticals [HTML] or [PDF] (Issued 5/2001, Posted 5/7/2001)
Procedural
1.
2.
3.
180-Day Generic Drug Exclusivity Under the Hatch-Waxman Amendments to the Federal Food, Drug, and
Cosmetic Act
(Issued 6/1998, Posted 6/22/1998)
Court Decisions, ANDA Approvals, and 180-Day Exclusivity Under the Hatch-Waxman Amendments to the
Federal Food, Drug, and Cosmetic Act [HTML] or [PDF] (Posted 3/27/2000)
Disclosure of Materials Provided to Advisory Committees in Connection with Open Advisory Committee
Meetings Convened by the Center for Drug Evaluation and Research Beginning on January 1, 2000 [HTML] or
4.
Enforcement Policy During Implementation of Section 503A of the Federal Food, Drug, and Cosmetic Act
Word Version (Issued 11/1998, Posted 11/20/1998)
5.
Fast Track Drug Development Programs – Designation, Development, and Application Review
or
Appendix 2
; Appendix 3
consisting of Mapp 6020.3 and SOPP 8405;and Appendix 4
[Appendices are
scanned copies, which will be replaced by final versions 11/18] (Issued 11/17/1998, Posted 11/17/1998)
6. Financial Disclosure by Clinical Investigators (3/27/2001)
7. Formal Dispute Resolution: Appeals Above the Division Level [HTML] or [PDF] (Issued 2/2000, Posted
3/6/2000)
8. Formal Meetings With Sponsors and Applicants for PDUFA Products [HTML] or [PDF] (Issued 2/2000, Posted
3/6/2000)
9. Implementation of Section 120 of the Food and Drug Administration Modernization Act of 1997-Advisory
Committees Wordperfect or Acrobat Version (Issued 10/1998, Posted 11/02/98)
10. Implementation of Section 126 of the Food and Drug Administration Modernization Act of 1997 - Elimination of
Certain Labeling Requirements
(Issued 7/1998, Posted 7/20/98)
11. Information Program on Clinical Trials for Serious or Life-Threatening Diseases and Conditions [HTML] or
12. Information Request and Discipline Review Letters Under the Prescription Drug User Fee Act [HTML] or [PDF]
(Issued 11/2001)
13. Levothyroxine Sodium Products Enforcement of August 14, 2001 Compliance Date and Submission of New
Applications [HTML] or [PDF] (Issued 7/2001, Posted 7/12/2001)
14. National Uniformity for Nonpresciption Drugs - Ingredient Listing for OTC Drugs
5/5/1998)
15. Potassium Iodide as a Thyroid Blocking Agent in Radiation Emergencies [HTML] or [PDF] (Issued 12/2001,
Posted 12/10/2001)
o KI in Radiation Emergencies-Questions and Answers [HTML] or [PDF] (Issued 12/20/2002, Posted
12/23/2002)
16. Reduction of Civil Money Penalties for Small Entities (Issued 3/20/2001)
17. Qualifying for Pediatric Exclusivity Under Section 505A of the Federal Food, Drug, and Cosmetic Act [HTML] or
18. Refusal to File
(Issued 7/12/1993, Posted 11/26/99)
19. Repeal of Section 507 of the Federal Food, Drug and Cosmetic Act
(Revised 5/1998, Posted 6/12/1998)
20. Special Protocol Assessment [HTML] or [PDF] (Issued 5/2002, Posted 5/16/2002)
21. Standards for Prompt Review of Efficacy Supplements
(Issued 5/15/1998, Posted 5/15/1998)
Procedural Draft
1.
2.
3.
4.
5.
6.
7.
Applications Covered by Section 505(b)(2) [HTML] or [PDF] or [Word] (Issued 10/1999, Posted 12/7/1999)
Collection of Race and Ethnicity Data in Clinical Trials [Word] or [PDF] (Issued 1/2003, Posted 1/23/2003)
Disclosing Information Provided to Advisory Committees in Connection with Open Advisory Committee
Meetings Related to the Testing or Approval of New Drugs and Convened by the Center for Drug Evaluation
and Research, Beginning on January 1, 2000 [HTML] or [PDF] (Issued 12/1999, Posted 12/22/1999)
Disclosure of Conflicts of Interest for Special Government Employees Participating in FDA Product Specific
Advisory Committees [HTML}or [PDF] (2/14/2002)
Forms for Registration of Producers of Drugs and Listing of Drugs in Commercial Distribution [HTML] or [PDF]
(5/14/2001)
Guidance for Federal Agencies and State and Local Governments Potassium Iodide Tablets Shelf Life
Extension [HTML] - [Word] - [PDF] (Posted 4/1/2003)
PET Drug Applications - Content and Format for NDAs and ANDAs (Issued 3/7/2000, Posted 3/7/2000)
o
o
o
o
8.
9.
Sample formats for chemistry, manufacturing, and controls sections
Sample formats for labeling
Sample formats for Form FDA 356h
Sample formats for user fee Form FDA 3397
(Issued 3/2001,
Postmarketing Safety Reporting for Human Drug and Biological Products Including Vaccines
Posted 3/9/2001)
Reports on the Status of Postmarketing Studies - Implementation of Section 130 of the Food and Drug
Administration Modernization Act of 1997 [HTML] or [PDF] (Posted 4/4/2001)
10. Submitting Debarment Certification Statements
(Issued 10/2/98, Posted 10/2/98)
11. Submitting Marketing Applications According to the ICH/CTD Format: General Considerations [PDF] (Issued
9/2001, Posted 9/5/2001)
12. The Use of Clinical Holds Following Clinical Investigator Misconduct
(Issued 4/2002, Posted 8/26/2002)
Small Entity Compliance Guides
1.
Sterility Requirement for Aqueous-Based Drug Products for Oral Inhalation — Small Entity Compliance Guide
[PDF] (Posted 11/7/2001)
User Fees
1.
2.
3.
4.
Classifying Resubmissions in Response to Action Letters
(Issued 5/14/1998, Posted 5/14/1998)
Fees-Exceed-the-Costs Waivers Under the Prescription Drug User Fee Act [HTML] or [PDF] (Issued 6/1999,
Posted 6/25/99)
Information Request and Discipline Review Letters Under the Prescription Drug User Fee Act [HTML] or [PDF]
(Issued 11/2001)
Submitting and Reviewing Complete Responses to Clinical Holds (Revised) [HTML] or [PDF] (Issued 10/2000,
Posted 10/25/2000)
User Fees (Draft)
1.
Submitting Separate Marketing Applications and Clinical Data for Purposes of Assessing User Fees [PDF]
(2/21/2001)
Also see Current Good Manufacturing Practice Regulations
Enforcement of the Postmarketing Adverse Drug Experience Reporting Regulations
(Posted 8/11/1997)
[Accessibility]
CDER Home Page | Search | Comment | What's New
12/17/2001
MEMBERGROUPS
Approx
Statistician
Members
Country
Group
Belgium
SBS/BVS (Société Belge de Statistique / Belgische Verenining voor
Statistiek - Biostatistics section)...........
.82
Denmark
DSBS (Dansk Selskab for Biofarmaceutisk Statistik)...........
.68
Finland
SSL (Statistikot Suomen lääketeollisuudessa)
.47
France
SFdS (Société Francaise de Statistique - Biopharmacy and Health
Group)...........
.168
APF (Arbeitsgruppe Pharmazeutische Forschung of German region of
International Biometric Society)...........
.272
Germany
Italy
BIAS (Biometristi dell'industria Farmaceutica Associati).....
.75
Netherlands
PSDM (werkgreop Pharmaceutische Statistiek en Datamanagement van
Verenining voor Statistiek - Biometrische Sectie)...........
.80
ABCif (Asociación de Biometria Clinica para la Investigacion
Farmaceutica)...........
.40
Spain
Sweden
FMS (Föreningen för Medicinsk Statistik)...........
Switserland
BBS (Basler Biometrische Sektion) of International Biometric
Society...........
United Kingdom
Total................
PSI (Statisticians in the Pharmaceutical Industry)...........
.105
.75
.1060
.2072
GENERAL INFORMATION
EFSPI, the European Federation of Statisticians in the Pharmaceutical Industry, is open to constituted groups
of statisticians. Eligibility for membership is open to one national group per country placing a major
emphasis on technical and scientific activities directed at statisticians who are working in or for the
pharmaceutical industry. Thus, EFSPI is engaged in statistical aspects of research, development, production,
and surveillance of drugs and medical devices. The constitutional objectives of the Federation are:
To promote professional standards of statistics and the standing of the statistical profession in
matters pertinent to the European pharmaceutical industry.
To offer a collective expert input on statistical matters to national and international authorities
and organisations.
To exchange information on and harmonise attitudes to the practice of statistics in the European
pharmaceutical industry and within the member groups.
After constitutional meetings since 1990, the Federation was officially launched in August 1992. There are
now 11 member groups including over 2000 pharmaceutical statisticians in Europe.
ACHIEVEMENTS (related to objectives of EFSPI)
To promote professional standards of statistics and the standing of the statistical profession in matters
pertinent to the European pharmaceutical industry:
EFSPI Working Party published report and paper on the qualifications and experience needed to
be a professional statistician in the pharmaceutical industry.
Published in Drug Information Journal, vol 33 (2), 1999.
Presented at conferences of International Statistical Institute (Helsinki, 1999), Royal Statistical
Society (Reading, 2000), PSI (Southampton, 1999), FMS (Umeå, Sweden, 1999), Italian
Statistical Society (Bressanone, Italy, 2000).
EFSPI set up clinical statistics meetings with DIA in London (1993), Edinburgh (1994), Nice
(1997) and Brussels (1999).
EFSPI was engaged in non-clinical statistics meetings with DIA in Bruges (1996), Nice (1998),
Montreux (2000).
EFSPI organised invited sessions at the International Statistical Institute conference in Florence
(1993) and Helsinki (1999).
٧
To offer a collective expert input on statistical matters to national and international authorities and
organisations:
Joint EFSPI/EFPIA working party provided European input on consultation drafts of ICH E9
(1997) and E10 (1999).
EFSPI co-ordinated input from national member groups to provide single European
pharmaceutical statistics view on ICH and CPMP consultation documents.
EFSPI was asked specifically for input by CPMP on Points to Consider document on Meta
Analysis and One Single Study.
EFSPI published a critical review of ICH E10 in Drug Information Journal, vol 35 (4), 2001.
To exchange information on and harmonise attitudes to the practice of statistics in the European
Pharmaceutical Industry and within member groups
EFSPI web-site contains links to all member groups web-sites.
There is a free exchange of information between Federation member groups in the areas of
meetings, courses, conferences, working parties, expert teams and ad hoc groups.
Activities organised by member groups are open to individual members of any Federation
member group.
EFSPI exchanges information with corresponding US and Japanese groups.
Informal networks established to facilitate communication between statisticians working in the
non-clinical and veterinary areas.
EFSPI
(European Federation of Statisticians in the
Pharmaceutical Industry)
• There is a working party for commenting
the guidelines on statistical issues
produced by ICH, EMEA, FDA
• For every concerned guideline, a person
responsible for collecting the comments
from the national organisations is
appointed. This person is responsible for
collating all the comments received from
his/her organisation and solving the
conflicting ones, when possible
PROCESS FOR RE VIE WING DOCUMENTS FOR EFSPI
1. Contact person chosen at EFSPI meeting for all upcoming regulatory documents to be
reviewed.
2. Regulatory document sent by the regulatory affairs chairman to the appropriate EFSPI contact
person.
3. EFSPI contact person sends a mail as soon as possible to all National contacts requesting that
they organise and collate the comments from their national organization on the document using
the standard form (reply form). The contact person should receive their comments at the latest
one month before comments are due.
4. Each country contact collects the comments from his/her member group, or delegates
someone else to do so. This can be done by a formal meeting, luncheon meeting, regulatory
affairs sub-committee, etc.
5. Before sending these comments to the EFSPI contact person, the country contact should
discern major comments from minor comments, and also separate any typing errors (see reply
form). Contradicting comments from within the national organisation should be avoided, or
clearly identified as such in the reply.
6. In case of many comments, the national contact may want to circulate the collated version to
his or her national group for approval before sending to the EFSPI contact person.
7. The EFSPI contact person then collates all country comments into one form again avoiding
contradicting comments where possible, and sends it to all national contacts. Usually only one
week is available for replies so the national contacts have to be aware of this.
8. The final document is then sent to the regulatory affairs chairman who forwards it to the
appropriate place. Copies of the final document are also sent to all EFSPI council members
who can then send them back to their national organisations if requested.
Draft 1 — 10 October 2002
page 1 of 1
Armonizzazione del comportamento
dei biostatistici nel contesto
decisionale dei Comitati etici
prof. Marubini
dr.ssa Patarnello
Armonizzazione
dei comportamenti dei
biostatistici nel contesto
decisionale dei
Comitati Etici
F. Patarnello, E. Marubini
Giornata su "Le competenze biostatistiche nell'ambito dei Comitati etici locali",
Roma 18 marzo 2003
1
Quale e’ la situazione?
...dalla parte dello Sponsor (da indagine)
• poco frequenti contatti diretti tra biostatistico CE e
Sponsor, e spesso senza qualificare da chi origina il
quesito
• poco frequenti quesiti sul disegno sperimentale, tranne
per il gruppo di controllo, in particolare sul placebo,
qualcuno su sample size, qualcuno su analisi statistica.
Frequentemente dopo chiarimento i punti non vengono
modificati. Prevalgono aspetti amministrativi,
assicurativi, sul consenso informato.
• possibili i casi di valutazioni diverse su protocolli simili in
tempi diversi o in CE diversi
• rarissime le richieste di risultati degli studi o sui risultati
• nessun caso di valutazioni preliminari del disegno dello
studio tra CE e Sponsor (tipo FDA)
Roma 18 marzo 2003
2
Quale e’ la situazione?
...dalla parte del CE (autocritica)
• Protocolli internazionali preconfezionati e non
modificabili
• Alcuni studi non correttamente dimensionati (studi
pilota?)
• Studi simili negli obiettivi ma con metodologia diversa e
sample size diversi
• Analisi statistica o non sufficientemente descritta o
troppo complessa, ma scarsamente riproducibile
• Scarsa attenzione alla diffusione e discussione dei
risultati e al controllo dell’effettiva analisi rispetto a
quella pianificata
Roma 18 marzo 2003
3
Armonizzazione: cosa non
significa?
• replicare sempre lo stesso disegno e la stessa analisi
(ogni studio e’ diverso)
• seguire le linee guida in modo burocratico
• seguire la letteratura in modo acritico
• comportarsi allo stesso modo in aree terapeutiche e in
fasi di sviluppo del farmaco diverse
Roma 18 marzo 2003
4
•
Armonizzazione: cosa
significa?
agire con coerenza nei confronti del problema e dell’obiettivo
dello studio
• costruire, studiare, capire, e applicare le linee guida non
rinunciando mai alla discussione di casi specifici o eccezionali
• essere informati sulle specificita’ metodologiche presenti
letteratura e non ragionare per ogni studio come se fosse il
primo
• specializzarsi in varie aree terapeutiche e in fasi di sviluppo
del farmaco diverse
• utilizzare la stessa accuratezza nella valutazione metodologica
indipendentemente dallo sponsor, dal disegno sperimentale
(caso degli studi osservazionali), dalla fase, dal tipo di
trattamento (farmaco, device, chirurgia) ma basandosi
sull’obiettivo
Roma 18 marzo 2003
5
Strumenti
• Formazione e aggiornamento comune
• Costruire le regole insieme
• Dialogo diretto tra biostatistici nei CE e presso
lo sponsor
• Trasparenza e disponibilita’ delle valutazioni
• Discussioni preliminari sui protocolli
• Attenzione ai risultati
• Controlli di qualita’ : rilevazione continua delle
differenze (dummy protocol?)
Roma 18 marzo 2003
6
Organizzazione dello scambio
informatizzato di commenti e pareri
prof. Grigoletto
dr.ssa Rinieri
Organizzazione dello
scambio informatizzato di
commenti e pareri
La funzione
dell’Osservatorio
Roma, 18 marzo 2003
Elisa Rinieri
Obiettivi
1
Il sistema è progettato per essere un
registro delle sperimentazioni cliniche sui
farmaci condotte in Italia che raccoglie tutte
le informazioni relative ad approvazione,
avvio e conclusione, incluse le informazioni
relative alle reazioni avverse
Obiettivi
2
Creare una rete per facilitare la comunicazione
dei dati e lo scambio di informazioni tra tutti i
partecipanti alla sperimentazione (Comitati Etici
dei centri partecipanti, Sponsor, autorità
competenti, Ministero della Salute)
Scambio di informazioni:
rete e sottoreti di comunicazione
Rete tra tutti i Comitati Etici e tutti gli Sponsor
Per ogni studio
creazione
di una sottorete per gli
Sponsor ed i Comitati
Etici coinvolti
Obiettivi
3
• Monitorare
l’intero
processo
della
sperimentazione
clinica
e
produrre
statistiche al fine di fornire al Ministero
della Salute una visione globale delle
sperimentazioni cliniche condotte in Italia
• Produrre un Rapporto Nazionale Annuale
delle sperimentazioni cliniche in Italia
Il sistema
L’Osservatorio Nazionale sulla Sperimentazione
Clinica è basato sulla metodologia
AMR - Advanced Multicenter Research
completamente web-based
E’ accessibile in Internet attraverso un browser
standard (Microsoft Internet Explorer, Netscape
Communicator).
Powered by
Web technology
Approvazione delle sperimentazioni in Italia
Fase
II, III
RICHIESTA
Sponsor
Rilascio del GdN dal
Comitato Etico del Centro
Coordinatore
o
(Ufficio Sperimentazione Clinica)
Parere favorevole
dell’Istituto Superiore
di Sanità sulla
sicurezza e qualità del
medicinale
SI
RICHIESTA
Fase IV
Sponsor
RICHIESTA
Sponsor
Fase I
SI
Richiesta di approvazione della sperimentazione
ai
Comitati Etici dei
Centri partecipanti
NO
SI
SI
SI
SI
INIZIO DELLO STUDIO
Inserimento elettronico dei dati
Per
ogni sperimentazione clinica lo sponsor
inserisce i dati richiesti direttamente attraverso
form elettroniche via web
In seguito al ricevimento della form elettronica
completata dallo sponsor, il Comitato Etico o
l’autorità competente dovrà validare e fornire
l’autorizzazione
alla
conduzione
della
sperimentazione clinica
Dopo l’inserimento, gli utenti non hanno più
accesso alla modifica dei dati, ogni ulteriore
modifica deve essere fatta dal Ministero della Salute
Accesso ai dati
Ogni utente, per essere autorizzato, deve
essere registrato tramite l’inserimento di
alcune informazioni identificative nella scheda
di registrazione accessibile via web
L’ufficio competente presso il Ministero della
Salute “valida” le informazioni e fornisce
all’utente userid e password
Profili utente
L’accesso al database prevede diversi profili utente:
• Sponsor/CRO
• Comitati Etici
• Regioni e Province Autonome
• ASL
• Data Review Committees
• Ufficio Sperimentazione Clinica (Ministero della Salute)
• Istituto Superiore di Sanità
Profili di accesso
• Ogni utente può accedere solo ai propri dati
• Solo il Ministero della Salute – Ufficio
Sperimentazione Clinica è autorizzato a vedere
tutto il Registro (Controllo Centrale)
• Solo gli sponsor ed i Comitati Etici sono
autorizzati all’inserimento dei dati
Raccolta delle informazioni
1
Per ogni studio, informazioni generali, per esempio:
• Titolo del Protocollo, codice, file ed emendamenti
• Sponsor e centri partecipanti
• Coordinatore dello studio
• Disegno dello studio (randomizzazione, nazionale,
internazionale, fase…)
• Codice di patologia (ICD), area terapeutica
• Durata e numero di pazienti previsti nello studio
• Informazioni sui farmaci (nome, dosaggio e
durata del trattamento, …)
• Eleggibilità (criteri di inclusione ed esclusione)
• Popolazione in studio (età, sesso, …)
Raccolta delle informazioni
2
Per ogni studio, dati amministrativi del flusso di
approvazione e stato corrente dello studio per ogni
centro coinvolto, per esempio:
• Dati di approvazione dello studio
• Data di apertura dello studio alla registrazione dei
pazienti
• Dati sullo stato corrente dello studio
• Raccolta dei pazienti
• Dati sulle reazioni avverse
• Pubblicazioni
OsSC - Database Globale
Registro
Registro
Registro
GdN
CRO
Sponsor
Registro
CT
Registro
Comitati
Etici
Registro
Registro
P.I.
Ospedali/Reparti
MMG/PLS
Procedure di qualità dei dati
• Filtri durante l’inserimento
• Dizionari standard per minimizzare l’uso di testo
libero
• Controlli automatici sulla consistenza dei dati
• Warning automatici
• Produzione di report predefiniti sulla qualità dei
dati (accuratezza, completezza, integrità)
Analisi dei dati
Il database è dotato di alcune funzioni di ricerca
che permettono l’individuazione di informazioni
specifiche attraverso parole chiave
Tools di data warehouse per analisi strategiche
Produzione automatica, in formato stampabile,
del Rapporto Nazionale
Sicurezza delle informazioni
Il sistema garantisce:
backup regolare su dispositivi elettronici di tutte
le informazioni, per permettere il ripristino in caso
di perdita o danneggiamento del database
tracking log (registrazione di ogni operazione
eseguita da ciascun utente)
audit trials (creazione di una banca dati storica di
ogni modifica effettuata con memorizzazione della
data e dell’utente)
Gli strumenti per lo scambio di
informazioni
Gli strumenti messi a disposizione dei Comitati
Etici per
creare una rete per facilitare la
comunicazione dei dati e lo scambio di
informazioni tra tutti i partecipanti alla
sperimentazione
Gli strumenti per lo scambio di
informazioni
Bacheca
Condivisione dei pareri sulle sperimentazioni
Comunicazione per gruppi di utenti che partecipano alla stessa
sperimentazione
Area contatti: comunicazioni ai Comitati Etici
Area contatti: forum fra tutti gli utenti dell’OsSC
Progress report
Sistema di messaggistica automatica
Forum con accesso riservato a gruppi particolari di utenti: p.e. ai
Comitati Etici con un componente biostatistico
Bacheca
1
Ogni Comitato Etico ha a disposizione uno spazio, accessibile a
tutti gli utenti dell’Osservatorio in cui pubblicare comunicazioni
e documenti.
La bacheca deve essere utilizzata per:
elenco documentazione da presentare al fine
dell'ottenimento dell'autorizzazione degli studi clinici;
calendario riunioni con ordine del giorno e data di scadenza
per la presentazione di documenti;
regolamento del Comitato Etico;
eventuale indirizzo del sito Web del Comitato Etico o
dell'Azienda;
segnalazione di convegni, congressi, giornate di studio e
seminari inerenti l'argomento.
Bacheca 2
Condivisione di pareri sulle
sperimentazioni 1
Ogni
Comitato
Etico
(limitatamente
alle
sperimentazioni a cui partecipa) ha accesso alle
informazioni
relative
allo
stato
di
approvazione della sperimentazione in
ciascuno degli altri Comitati Etici partecipanti
sperimentazioni 2
sperimentazioni 3
sperimentazioni 4
sperimentazioni 5
Comunicazioni per gruppi di utenti che
partecipano alla stessa sperimentazione
1
Ogni
Comitato
Etico
(limitatamente
alle
sperimentazioni a cui partecipa) può accedere ad
uno spazio riservato alle comunicazioni
accessibili solo al gruppo di utenti (sponsor,
Comitato Etico del centro coortinatore,
Comitati Etici dei centri satellite) che
partecipa ad una specifica sperimentazione
partecipano alla stessa sperimentazione 2
Area Contatti:
comunicazioni ai Comitati Etici
1
Nell’area contatti è disponibile la lista di tutti i
Comitati
Etici
abilitati
all’utilizzo
dell’Osservatorio con possibilità di inviare una
e-mail ad un singolo Comitato Etico oppure
a tutti i Comitati Etici
Area Contatti:
comunicazioni ai Comitati Etici
2
Area Contatti:
comunicazioni ai Comitati Etici 3
Area Contatti:
forum fra tutti gli utenti dell’Osservatorio
Il Forum è uno spazio in cui inserire
messaggi e quesiti di interesse per tutti gli
utenti dell'Osservatorio; le risposte ai quesiti
possono da uno qualsiasi degli utenti
dell'Osservatorio e vengono pubblicate nel
Forum
1
Area Contatti:
forum fra tutti gli utenti dell’Osservatorio 2
Area Contatti:
Area Contatti:
Area Contatti:
Progress Report
1
Ciascun
Comitato Etico (limitatamente alle
sperimentazioni a cui partecipa) ha accesso ad uno
schema riassuntivo per il monitoraggio dello
stato di avanzamento delle sperimentazioni
Progress Report 2
Progress Report 3
Sistema di messaggistica automatica
1
Il sistema prevede l’invio di messaggi
automatici per la segnalazione ai Comitati
Etici della presenza di nuovi studi da
approvare all’interno dell’Osservatorio
Forum con accesso riservato a gruppi
particolari di utenti 1
In funzione di specifiche esigenze è possibile
creare sistemi per comunicazioni riservate
a gruppi specifici di utenti. Ad esempio
potrebbe essere creato un forum riservato ai
Comitati
Etici
con
un
componente
“biostatistico”
Scambio informatizzato di informazioni:
rete e sottoreti di comunicazione
Sottorete di
comunicazione
fra biostatistici
Rete tra tutti i Comitati Etici e tutti gli Sponsor
Per ogni studio
creazione
di una sottorete per gli
Sponsor ed i Comitati
Etici coinvolti

٧ - AIFA Agenzia Italiana del Farmaco

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