ExL Pharma’s Multiple Comparisons in Clinical Trials HighlightsJanuary 25-27, 2010Rockville, MD

Subgroup analyses in Pharmaceutical Development- Must we always adjust

for multiplicity?

What Does the FDA Say About Subgroup Analyses?•Not Much!•The need for conducting subgroups

analyses is acknowledged •No methodological guidance is provided•Subgroup analyses are lumped together

with other multiplicity issues

FDA Position on Subgroup Analyses

• Subgroups of interest must be pre-specified in the protocol• Inferences about subgroups following the ITT analysis is

subject to multiplicity Type I error adjustment• Generally, subgroup analyses are exploratory only

▫ Hypotheses generation▫ Identify heterogeneity w.r.t baseline, demographic, geographic

variables• Generally, NDA approval requires significance of the

primary endpoint in ITT• Significance in pre-specified subgroup is not sufficient

Fundamental Question:

Do the problems associated with subgroup analyses raise multiplicity

issues?

MultiplicityMultiplicity issue arises when a single inference is based on

multiple repeated testing▫ Interim analyses (multiple looks)▫ Multiple comparisons (e.g. multiple doses of a drug)▫ Multiple endpoints

Error to be controlled = Family-wise Error Rate

Multiple Comparisons Paradigm

Regulatory claim:Drug is efficacious Patient population A

Stat Decision Rule:Drug is efficacious if

Sig. on V1, OR Sig. on V2, ORSig. on V3, etc.

Testing

V1 Sig?

V2 Sig?

V3 Sig?

Yes

Yes

Yes

EFFICACIOUS

Control “family-wise” Error Rate

Subgroup AnalysisExample:

• Placebo-controlled global trial of a new ACE inhibitor

• Sponsor is interested in investigating the drug’s efficacy in African patients

• Randomization stratified by country• Primary efficacy variable – DiPB• Target population – Patients with moderate

hypertension

Analysis Strategy

•Test for efficacy in the ITT•Proceed to test in the subgroup of

African Patients

Possible Outcomes

P 0.05P > 0.05

P 0.05P > 0.05P 0.05P > 0.05

Test in SubgroupTest in Subgroup

Test in ITT

A B C D

Inferences

P 0.05P > 0.05

P 0.05P > 0.05P 0.05P > 0.05

Test in SubgroupTest in Subgroup

Test in ITT

Treatment Selection in Multi-Armed Trials Using Confirmatory Adaptive Designs

The term adaptive

• Adaptive randomization

• Adaptive test selection

• Adaptive dose selection

• Bayesian adaptive designs

• Confirmatory adaptive designs

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Multi-armed designs• Consider many-to-one comparisons, e.g., G treatment

arms and one control, normal case.

• In an interim stage a treatment arm is selected based on data observed so far.

• Not only selection procedures, but also other adaptive strategies (e.g., sample size reassessment) can be performed.

• Application, e.g., within an “Adaptive seamless designs” using the combination testing principle, but investigation of more than one dose in phase III is also encouraged.

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Standard2 phases

AdaptiveSeamlessDesign

Plan & DesignPhase III

Dose Selection

Learning

A

B

C

DControl

Confirming

Learning, Selecting and Confirming

Plan & DesignPhase IIb

Plan & DesignPhase IIb and III

Adaptive seamless designs

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A

B

C

DControl

ExampleComparison of three test procedures

•Inverse normal Dunnett

•Pure conditional Dunnett

•Separate stage conditional Dunnett

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Comparison of the three procedures

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Design: two-stage, = 0.025 one-sided, u1 = , u2 = 1.96 linear dose-reponse relationship with drift

120 i.e., ,20;20 220

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12

11

10 Nnnnnnn S

140 i.e., ,20;20 2220

13

12

11

10 21

Nnnnnnnn SS

- always select the two best:

- select all:

160 i.e., ,20;20 23

22

21

20

13

12

11

10 Nnnnnnnnn

- always select the best:

Consider three selection procedures:

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19

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The comparison shows that • the conditional second-stage Dunnett test performs

best

• it is identical with the conventional Dunnett test if no adaptations were performed

• becomes complicated if, e.g., ▫ allocation is not constant

▫ variance is unknown

• the inverse normal technique is not optimum but enables early stopping and more general adaptations

• is straightforward if, e.g., ▫ allocation is not constant

▫ variance is unknown

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Clinical Trials Conferences, please visit www.exlpharma.com