ExLPharma’s Multiple Comparisons in Clinical Trials Highlights<br />January 25-27, 2010<br />Rockville, MD<br />
Subgroup analyses in Pharmaceutical Development- Must we always adjust for multiplicity?<br />
What Does the FDA Say About Subgroup Analyses?<br />Not Much!<br />The need for conducting subgroups analyses is acknowled...
FDA Position on Subgroup Analyses<br />Subgroups of interest must be pre-specified in the protocol<br />Inferences about s...
Fundamental Question:<br />Do the problems associated with subgroup analyses raise multiplicity issues?<br />
Multiplicity<br />Multiplicity issue arises when a single inference is based on multiple repeated testing<br />Interim ana...
Stat Decision Rule:<br />Drug is efficacious if<br />Sig. on V1, OR <br />Sig. on V2, OR<br />Sig. on V3, etc.<br />Testin...
Subgroup Analysis<br />Example:<br />Placebo-controlled global trial of a new ACE inhibitor<br />Sponsor is interested in ...
Analysis Strategy<br />Test for efficacy in the ITT<br />Proceed to test in the subgroup of African Patients<br />
Possible Outcomes<br />Test in ITT<br />P  0.05<br />P > 0.05<br />Test in Subgroup<br />Test in Subgroup<br />P  0.05<b...
Inferences<br />Test in ITT<br />P  0.05<br />P > 0.05<br />Test in Subgroup<br />Test in Subgroup<br />P  0.05<br />P >...
Treatment Selection in Multi-Armed Trials Using Confirmatory Adaptive Designs<br />
The term adaptive<br />Adaptive randomization<br />Adaptive test selection<br />Adaptive dose selection<br />Bayesian adap...
Multi-armed designs<br />Considermany-to-one comparisons, e.g., G treatment arms and one control, normal case.<br />In an ...
A<br />A<br />B<br />B<br />C<br />C<br />D<br />D<br />Control<br />Control<br />Adaptive seamless designs<br />Learning<...
Example<br />Comparison of three test procedures<br />Inverse normal Dunnett<br />Pure conditionalDunnett<br />Separate st...
Comparison of the three procedures<br />17<br />Design: two-stage,  = 0.025 one-sided, u1 = , u2 = 1.96 linear dose-repo...
18<br />
19<br />
20<br />
The comparisonshowsthat<br />theconditionalsecond-stageDunnetttestperformsbest<br />itisidenticalwiththeconventionalDunnet...
Still have any questions? For additional information on ExLPharma’s Multiple Comparisons in Clinical Trials Conferences, p...
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Highlights from ExL Pharma's Multiple Comparisons in Clinical Trials

  1. 1. ExLPharma’s Multiple Comparisons in Clinical Trials Highlights<br />January 25-27, 2010<br />Rockville, MD<br />
  2. 2. Subgroup analyses in Pharmaceutical Development- Must we always adjust for multiplicity?<br />
  3. 3. What Does the FDA Say About Subgroup Analyses?<br />Not Much!<br />The need for conducting subgroups analyses is acknowledged <br />No methodological guidance is provided<br />Subgroup analyses are lumped together with other multiplicity issues <br />
  4. 4. FDA Position on Subgroup Analyses<br />Subgroups of interest must be pre-specified in the protocol<br />Inferences about subgroups following the ITT analysis is subject to multiplicity Type I error adjustment<br />Generally, subgroup analyses are exploratory only<br />Hypotheses generation<br />Identify heterogeneity w.r.t baseline, demographic, geographic variables<br />Generally, NDA approval requires significance of the primary endpoint in ITT<br />Significance in pre-specified subgroup is not sufficient<br />
  5. 5. Fundamental Question:<br />Do the problems associated with subgroup analyses raise multiplicity issues?<br />
  6. 6. Multiplicity<br />Multiplicity issue arises when a single inference is based on multiple repeated testing<br />Interim analyses (multiple looks)<br />Multiple comparisons (e.g. multiple doses of a drug)<br />Multiple endpoints<br />Error to be controlled = Family-wise Error Rate<br />
  7. 7. Stat Decision Rule:<br />Drug is efficacious if<br />Sig. on V1, OR <br />Sig. on V2, OR<br />Sig. on V3, etc.<br />Testing<br />E<br />F<br />F<br />I<br />C<br />A<br />C<br />I<br />O<br />U<br />S<br />V1 Sig?<br />Yes<br />Control “family-wise” Error Rate  <br />V2 Sig?<br />Yes<br />V3 Sig?<br />Yes<br />Multiple Comparisons Paradigm<br />Regulatory claim:<br />Drug is efficacious <br />Patient population A<br />
  8. 8. Subgroup Analysis<br />Example:<br />Placebo-controlled global trial of a new ACE inhibitor<br />Sponsor is interested in investigating the drug’s efficacy in African patients<br />Randomization stratified by country<br />Primary efficacy variable – DiPB<br />Target population – Patients with moderate hypertension<br />
  9. 9. Analysis Strategy<br />Test for efficacy in the ITT<br />Proceed to test in the subgroup of African Patients<br />
  10. 10. Possible Outcomes<br />Test in ITT<br />P  0.05<br />P > 0.05<br />Test in Subgroup<br />Test in Subgroup<br />P  0.05<br />P > 0.05<br />P  0.05<br />P > 0.05<br />A<br />B<br />C<br />D<br />
  11. 11. Inferences<br />Test in ITT<br />P  0.05<br />P > 0.05<br />Test in Subgroup<br />Test in Subgroup<br />P  0.05<br />P > 0.05<br />P  0.05<br />P > 0.05<br />
  12. 12. Treatment Selection in Multi-Armed Trials Using Confirmatory Adaptive Designs<br />
  13. 13. The term adaptive<br />Adaptive randomization<br />Adaptive test selection<br />Adaptive dose selection<br />Bayesian adaptive designs<br />Confirmatory adaptive designs<br />13<br />
  14. 14. Multi-armed designs<br />Considermany-to-one comparisons, e.g., G treatment arms and one control, normal case.<br />In an interim stage a treatment arm is selected based on data observed so far.<br />Not only selection procedures, but also other adaptive strategies (e.g., sample size reassessment) can be performed. <br />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.<br />14<br />
  15. 15. A<br />A<br />B<br />B<br />C<br />C<br />D<br />D<br />Control<br />Control<br />Adaptive seamless designs<br />Learning<br />Standard<br />2 phases<br />Confirming<br />Plan & <br />Design<br />Phase III<br />Plan & Design<br />Phase IIb<br />Adaptive<br />Seamless<br />Design<br />Learning, Selecting and Confirming<br />Plan & Design<br />Phase IIb and III<br />Dose Selection<br />15<br />
  16. 16. Example<br />Comparison of three test procedures<br />Inverse normal Dunnett<br />Pure conditionalDunnett<br />Separate stageconditionalDunnett<br />16<br />
  17. 17. Comparison of the three procedures<br />17<br />Design: two-stage,  = 0.025 one-sided, u1 = , u2 = 1.96 linear dose-reponse relationship withdrift<br />Consider three selection procedures:<br />- always select the best:<br />- always select the two best: <br />- select all: <br />
  18. 18. 18<br />
  19. 19. 19<br />
  20. 20. 20<br />
  21. 21. The comparisonshowsthat<br />theconditionalsecond-stageDunnetttestperformsbest<br />itisidenticalwiththeconventionalDunnetttestifnoadaptationswereperformed<br />becomescomplicatedif, e.g., <br />allocationis not constant<br />varianceisunknown<br />the inverse normal techniqueis not optimum but enablesearlystoppingandmoregeneraladaptations<br />isstraightforwardif, e.g., <br />allocationis not constant<br />varianceisunknown<br />21<br />
  22. 22. Still have any questions? For additional information on ExLPharma’s Multiple Comparisons in Clinical Trials Conferences, please visit www.exlpharma.com<br />

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