title: Detecting moderator effects using subgroup analyses.
abstract: 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.
authors.affiliation: Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, SPH2, 4th Floor, Boston, MA 02115, USA. rwang@hsph.harvard.edu
authors.forename: Rui
authors.initials: R
authors.lastname: Wang
authors.forename: James H
authors.initials: JH
authors.lastname: Ware
mesh_terms: D006801:Humans; D015588:Observer Variation; D011314:Preventive Health Services / Q000458:organization & administration*