Subgroup analysis in therapeutic trials

Therapy in cardiology must be based on solid scientific evidence, obtained in randomized controlled trials (RCTs), since this is the best design that proves causality in medicine. The applicability of clinical trial results to the individual patient depends on a rigorous set of rules that can be summarized in the question "Could my patient have been enrolled in this trial?" If the answer to this question is affirmative, then the possibility of applying the trial results is greater. If it is negative, then the cardiologist should exercise caution in his or her decision. In an RCT--of whatever size--it is almost always possible to identify subgroups of patients that show significant differences in treatment effect: for example, studies have shown that, in patients with non-rheumatic atrial fibrillation, oral anticoagulants should be given to prevent stroke, except in those younger than 65 years with no additional risk factors, for whom aspirin is a better option. Subgroup analysis is important because, when the magnitude of the difference is both real and large, it may influence patient management. This analysis should be done with great care, since it has the potential to lead to major errors in data interpretation, identifying differences in treatment effects that are due to chance alone or, more frequently, have no clinical significance. In this article we present a set of guidelines that enable the cardiologist to assess the credibility of an analysis that shows apparent differences in treatment effects across subgroups.