A statistical primer on subgroup analyses

Abstract

Resources for clinical research are limited. With increasing demand for patient-centred care, which is growing into an integral component of modern medicine, studying outcomes of patients with specific clinical characteristics is becoming increasingly important. Given the high cost of clinical trials and the time it takes to complete an investigation, it has become compulsory for investigators to assess not only treatment effects between the main randomized groups but also to try to identify clinically relevant subgroups that may particularly benefit from specific treatments. Publications of subgroup analyses turned out to be prevalent, and more importantly, these findings play a significant role in strategic planning and decision-making processes. Therefore, raising awareness among clinicians about the concepts and values of subgroup analysis is an aspect of improving patient outcomes. In this statistical primer, we give a broad introduction to the topic of subgroup analysis in scientific research. We furthermore discuss the concept of subgroup analysis; the motivation for assessing subgroups; the types of subgroup analyses and the paradigm of hypothesis-generating research; the proper statistical methods for the examination of subgroup effects; and the optimal approach for interpretation of results. Finally, this review establishes the comprehensive users' guide for analysing and reporting subgroup studies on a point-by-point basis, using real-world examples that may help readers to gain experience to pursue their own subgroup analyses or interpret those of others.

Keywords: Clinical trials; Multiple testing; Statistics; Stratification; Subgroup analysis; Tests of interaction.