In the analysis of clinical trials, it is often important to investigate whether treatment effects vary among groups of patients defined by individual characteristics. These “subgroup analyses” can provide information about how best to use a new treatment. However, subgroup analyses can be misleading if they test data driven hypotheses, employ inappropriate statistical methods, or fail to account for multiple testing. We discuss sound methods for conducting subgroup analyses, the concept of heterogeneity and its dependence on the metric for measuring treatment effects, issues of multiple comparisons, and the type of questions that would lead to subgroup analyses and how scientific goals may affect a clinical trial at the design stage. Finally, we discuss subgroup analyses based on post-baseline factors and the complexity associated with this type of subgroup analysis.