The discussion begins with multiplicity issues arising in the context of exploratory subgroup analysis, including principled approaches to subgroup search that are applied as part of subgroup exploration exercises as well as in adaptive biomarker-driven designs. Frequently, investigators improperly test every subgroup, which opens the door to chance findings. 64 multiplicity issues encountered in clinical trials are described. The discussion begins with multiplicity issues arising in the context of exploratory subgroup analysis, including principled approaches to subgroup search that are applied as part of subgroup exploration exercises as well as in adaptive biomarker‐driven designs. This paper deals with the general topic of subgroup analysis in late-stage clinical trials with emphasis on multiplicity considerations. In the previous article, I discussed subgroup analyses. These approaches emphasize fundamental statistical principles, including the importance of performing multiplicity adjustments to account for selection bias inherent in subgroup search. Also, researchers conduct unplanned subgroup and interim analyses. By testing enough subgroups, a false-positive result will probably emerge by chance alone. If the number of subgroup analyses cannot be limited, the effect of multiplicity should be carefully considered when a subgroup difference is claimed. Specific issues, including adjustment of 65 elementary hypothesis tests for multiplicity, m ultiple primary endpoints, analysis sets and alternative 66 . Principled approaches to exploratory subgroup analysis based on recent advances in machine learning and data mining have been developed to address this criticism. In this article and the next, we will consider other aspects of clinical trials that lead to multiplicity of data when multiple hypotheses are tested at the same time. Proper analysis dissipates much of the multiplicity problem with subgroup analyses. statistical methods are addressed. The discussion begins with multiplicity issues arising in the context of exploratory subgroup analysis, including principled approaches to subgroup search that are applied as part of subgroup exploration exercises as well as in adaptive biomarker-driven designs. 1 Investigators frequently data-dredge by doing many subgroup analyses and undertaking repeated interim analyses. In general, we discourage subgr … 14 For the 102 studies with claims of subgroup analyses in our analysis, the authors discussed concerns about multiplicity in only six articles. Subgroup analyses satisfying the three aforementioned conditions are considered as having a confirmatory nature, thereby requiring multiplicity adjustments. Multiplicity issues from subgroup and interim analyses pose similar problems to those from multiple endpoints and treatment groups. Multiplicity Considerations in Confirmatory Subgroup Analyses Frank Bretz European Statistical Meeting on Subgroup Analyses Brussels, November 30, 2012 Investigators might undertake many analyses but only report the significant effects, distorting the medical literature. Subgroup analyses can pose serious multiplicity concerns. For example, breaking down age at baseline into four categories yields four tests just on that characteristic ( …

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