Subgroup Analysis

Abstract

AbstractSubgroup analysis refers to the practice of attempting to determine whether and how treatment effects vary among subgroups of subjects who are studied in intervention studies. This article reviews the variety of statistical methods that have been used and developed for subgroup analyses. These methods include testing null hypotheses of no treatment effects within subgroups,P‐value adjustments for multiplicity, confidence intervals for subgroup‐specific treatment effects, interaction tests, qualitative interaction tests, and empirical Bayes and fully Bayesian methods. The article addresses the distinction between preplanned subgroup analyses in which experiment‐wise type I error rate is controlled and an unplanned data‐dredging exercise for the purpose of generating hypotheses to be tested in subsequent studies. The article emphasizes the importance of restricting inferential methods to a very limited number of prespecified subgroups based on strong biological rationale or reports from other clinical trials. The article discusses the central nature of a limited number of planned highly focused subgroup analyses for pharmacogenomic clinical trials. The article also addresses the limited reliably evaluating consistency of treatment effects among subgroups in positive clinical trials, including center effects in multi‐center trials, and the article discusses limitations imposed by sample size for subgroup analyses and the role of meta‐analysis in subgroup analyses.