1. ExLPharma’s Multiple Comparisons in Clinical Trials Highlights January 25-27, 2010 Rockville, MD

2. Subgroup analyses in Pharmaceutical Development- Must we always adjust for multiplicity?

3. 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

4. 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

5. Fundamental Question: Do the problems associated with subgroup analyses raise multiplicity issues?

6. Multiplicity Multiplicity 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

7. Stat Decision Rule: Drug is efficacious if Sig. on V1, OR Sig. on V2, OR Sig. on V3, etc. Testing E F F I C A C I O U S V1 Sig? Yes Control “family-wise” Error Rate   V2 Sig? Yes V3 Sig? Yes Multiple Comparisons Paradigm Regulatory claim: Drug is efficacious Patient population A

8. Subgroup Analysis Example: 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

9. Analysis Strategy Test for efficacy in the ITT Proceed to test in the subgroup of African Patients

10. Possible Outcomes Test in ITT P  0.05 P > 0.05 Test in Subgroup Test in Subgroup P  0.05 P > 0.05 P  0.05 P > 0.05 A B C D

11. Inferences Test in ITT P  0.05 P > 0.05 Test in Subgroup Test in Subgroup P  0.05 P > 0.05 P  0.05 P > 0.05

12. Treatment Selection in Multi-Armed Trials Using Confirmatory Adaptive Designs

13. The term adaptive Adaptive randomization Adaptive test selection Adaptive dose selection Bayesian adaptive designs Confirmatory adaptive designs 13

14. Multi-armed designs Considermany-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. 14

15. A A B B C C D D Control Control Adaptive seamless designs Learning Standard 2 phases Confirming Plan & Design Phase III Plan & Design Phase IIb Adaptive Seamless Design Learning, Selecting and Confirming Plan & Design Phase IIb and III Dose Selection 15

16. Example Comparison of three test procedures Inverse normal Dunnett Pure conditionalDunnett Separate stageconditionalDunnett 16

17. Comparison of the three procedures 17 Design: two-stage,  = 0.025 one-sided, u1 = , u2 = 1.96 linear dose-reponse relationship withdrift Consider three selection procedures: - always select the best: - always select the two best: - select all:

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21. The comparisonshowsthat theconditionalsecond-stageDunnetttestperformsbest itisidenticalwiththeconventionalDunnetttestifnoadaptationswereperformed becomescomplicatedif, e.g., allocationis not constant varianceisunknown the inverse normal techniqueis not optimum but enablesearlystoppingandmoregeneraladaptations isstraightforwardif, e.g., allocationis not constant varianceisunknown 21

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