Subgroup analyses in clinical research: too tempting?

Abstract

Abstract In many biomedical studies, subgroup analyses are performed to identify subgroups of patients in whom a treatment is most effective, or a risk factor has the largest effect. While both are referred to as subgroup analysis, it is important to distinguish between the estimation of effects within subgroups and the comparison of effects across subgroups. Both are discussed, and we outline the implications regarding sample size and statistical methods for estimation of effects. Also, the risk of false-positive findings—which potentially increases with subgroup analysis—is discussed, as well as the distinction between effect modification and interaction.