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A flexible strategy for testing subgroups and overall population
Author(s) -
Alosh Mohamed,
Huque Mohammad F.
Publication year - 2008
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.3461
Subject(s) - subgroup analysis , population , type i and type ii errors , statistics , consistency (knowledge bases) , a priori and a posteriori , statistical power , multiple comparisons problem , econometrics , computer science , mathematics , medicine , artificial intelligence , confidence interval , philosophy , environmental health , epistemology
Subgroup analyses in addition to the total study population analysis are common in clinical trials. However, it is well recognized that findings from subgroup analyses do not provide confirmatory evidence for subgroup treatment effects without placing a priori criteria for ensuring that their findings are scientifically sound. In this paper we address some of the common pitfalls of subgroup analyses. Subgroups analyses inherently have low power for detecting treatment effects. We investigate the power interplay for a subgroup analysis and that for the total study population and list factors that impact the power of a subgroup analysis. Then we introduce a flexible statistical strategy for testing a pre‐specified sequence of hypotheses for both the overall and a subgroup. The proposed method strongly controls the familywise Type I error rate and enjoys higher power than other traditional methods. This testing strategy allows testing for a subgroup once a pre‐specified degree of consistency in the efficacy findings between the subgroup and the overall study population is met. In addition, it accounts for the dependency between test statistics for the subgroup and the overall study population. We discuss the power performance of this new method and provide significance levels for subgroup analysis. Finally, we illustrate its application through retrospective analysis of data from three published clinical trials. Copyright © 2008 John Wiley & Sons, Ltd.