Statistical Considerations on Subgroup Analysis in Clinical Trials

Abstract

The objective of subgroup analysis of a clinical trial is to investigate consistency or heterogeneity of the treatment effect across subgroups, defined based on background characteristics. As such, subgroup analysis plays an essential role in the interpretation of the clinical trial findings. Consistency of treatment effect across trial subgroups indicates that the average treatment effect is in general applicable regardless of the specific background characteristics. Substantial heterogeneity in treatment effect may be indicative that treatment benefit pertains only to a subset of the population. However, heterogeneity in the observed treatment effect across subgroups can arise due to chance as a result of partitioning the population into several subgroups. Furthermore, as it is known, clinical trials are generally not powered for detecting heterogeneity, thus statistical tests may miss detection of existing heterogeneity due to low power. In this article, we aim to: (i) outline the major issues underlying subgroup analysis in clinical trials and provide general statistical guidance for interpretation of its findings, (ii) provide statistical perspectives on the design and analysis of a clinical trial that aims for establishing efficacy in a targeted subgroup along with that of its overall population, and (iii) highlight some of the underlying assumptions and issues relevant to Bayesian subgroup analysis, subgroup considerations for noninferiority trials, personalized medicine, subgroup misclassification, and finally, subgroup analysis for safety assessment.