Detecting Moderator Effects Using Subgroup Analyses

In the analysis of prevention and intervention studies, it is often important to investigate whether treatment effects vary
among subgroups of patients defined by individual characteristics. These “subgroup analyses” can provide information about
how best to use a new prevention or intervention program. However, subgroup analyses can be misleading if they test data-driven
hypotheses, employ inappropriate statistical methods, or fail to account for multiple testing. These problems have led to
a general suspicion of findings from subgroup analyses. This article discusses sound methods for conducting subgroup analyses
to detect moderators. Multiple authors have argued that, to assess whether a treatment effect varies across subgroups defined
by patient characteristics, analyses should be based on tests for interaction rather than treatment comparisons within the
subgroups. We discuss the concept of heterogeneity and its dependence on the metric used to describe treatment effects. We
discuss issues of multiple comparisons related to subgroup analyses and the importance of considering multiplicity in the
interpretation of results. We also discuss the types of questions that would lead to subgroup analyses and how different scientific
goals may affect the study at the design stage. Finally, we discuss subgroup analyses based on post-baseline factors and the
complexity associated with this type of subgroup analysis.