An overview of statistical planning to address subgroups in confirmatory clinical trials

The effects of treatments within demographic and clinical subgroups of patients are of major interest in most confirmatory clinical trials. Potential factors for defining subgroups include gender, age, disease severity, and geographic region. A major statistical issue for the interpretation of treatment comparisons for subgroups is whether the role of a subgroup is inferential, supportive, or exploratory through respectively corresponding to a primary, key secondary, or hypothesis-generating assessment. This article discusses statistical planning to control type 1 error for the multiple comparisons that correspond to the scope of prespecified inferential subgroups, and it provides some suggestions for addressing the type 2 error that can pertain to prespecified supportive subgroups. Treatment comparisons for exploratory subgroups without a priori specification should always have a very cautious interpretation that accounts for how random variation can influence their pattern of results, although the suggested methods for supportive subgroups can be helpful in this light.