Analysis and interpretation of treatment effects in subgroups of patients in randomized clinical trials

Abstract

A key principle for interpretation of subgroup results is that quantitative interactions (differences in degree) are much more likely than qualitative interactions (differences in kind). Quantitative interactions are likely to be truly present whether or not they are apparent, whereas apparent qualitative interactions should generally be disbelieved as they have usually not been replicated consistently. Therefore, the overall trial result is usually a better guide to the direction of effect in subgroups than the apparent effect observed within a subgroup. Failure to specify prior hypotheses, to account for multiple comparisons, or to correct P values increases the chance of finding spurious subgroup effects. Conversely, inadequate sample size, classification of patients into the wrong subgroup, and low power of tests of interaction make finding true subgroup effects difficult. We recommend examining the architecture of the entire set of subgroups within a trial, analyzing similar subgroups across independent trials, and interpreting the evidence in the context of known biologic mechanisms and patient prognosis.