Research and scholarly methods: Subgroup analysis

Abstract

Subgroup analysis is designed to evaluate whether an intervention has differing effects according to baseline characteristics of participants in clinical trials. This approach can help identify subgroups of patient populations, within a single trial or across multiple trials (meta-analysis), that may benefit from the intervention or can be hypothesis-generating for future trials. Properly prespecified subgroup analyses can increase the effect size of subgroups and the legitimacy of P-values in determining significance and hypothesis testing. However, many pitfalls and limitations exist and must be recognized, including post hoc data-generating, multiple testing, selective reporting, and insufficient sample size. Failure to account for these limitations could lead to erroneous interpretations that adversely impact clinical decision-making. This review will provide a general overview, best practices, and pitfalls to avoid for investigators involved in the design, conduct, or interpretation of subgroup analyses.