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Highlights from ExL Pharma's Multiple Comparisons in Clinical Trials
Highlights from ExL Pharma's Multiple Comparisons in Clinical Trials
Highlights from ExL Pharma's Multiple Comparisons in Clinical Trials
Highlights from ExL Pharma's Multiple Comparisons in Clinical Trials
Highlights from ExL Pharma's Multiple Comparisons in Clinical Trials
Highlights from ExL Pharma's Multiple Comparisons in Clinical Trials
Highlights from ExL Pharma's Multiple Comparisons in Clinical Trials
Highlights from ExL Pharma's Multiple Comparisons in Clinical Trials
Highlights from ExL Pharma's Multiple Comparisons in Clinical Trials
Highlights from ExL Pharma's Multiple Comparisons in Clinical Trials
Highlights from ExL Pharma's Multiple Comparisons in Clinical Trials
Highlights from ExL Pharma's Multiple Comparisons in Clinical Trials
Highlights from ExL Pharma's Multiple Comparisons in Clinical Trials
Highlights from ExL Pharma's Multiple Comparisons in Clinical Trials
Highlights from ExL Pharma's Multiple Comparisons in Clinical Trials
Highlights from ExL Pharma's Multiple Comparisons in Clinical Trials
Highlights from ExL Pharma's Multiple Comparisons in Clinical Trials
Highlights from ExL Pharma's Multiple Comparisons in Clinical Trials
Highlights from ExL Pharma's Multiple Comparisons in Clinical Trials
Highlights from ExL Pharma's Multiple Comparisons in Clinical Trials
Highlights from ExL Pharma's Multiple Comparisons in Clinical Trials
Highlights from ExL Pharma's Multiple Comparisons in Clinical Trials
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Highlights from ExL Pharma's Multiple Comparisons in Clinical Trials

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  • 1. ExLPharma’s Multiple Comparisons in Clinical Trials Highlights<br />January 25-27, 2010<br />Rockville, MD<br />
  • 2. Subgroup analyses in Pharmaceutical Development- Must we always adjust for multiplicity?<br />
  • 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. 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. Fundamental Question:<br />Do the problems associated with subgroup analyses raise multiplicity issues?<br />
  • 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. 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. 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. Analysis Strategy<br />Test for efficacy in the ITT<br />Proceed to test in the subgroup of African Patients<br />
  • 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. 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. Treatment Selection in Multi-Armed Trials Using Confirmatory Adaptive Designs<br />
  • 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. 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. 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. Example<br />Comparison of three test procedures<br />Inverse normal Dunnett<br />Pure conditionalDunnett<br />Separate stageconditionalDunnett<br />16<br />
  • 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<br />
  • 19. 19<br />
  • 20. 20<br />
  • 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. 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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