A framework to assess the value of subgroup analyses when the overall treatment effect is significant

Although subgroup analysis has been developed and widely used for many years, it is still not clear whether we should perform and how to perform such subgroup analyses when the overall treatment effect is significant. In this paper, we develop a framework to assess and compute the long-term impact of different strategies to perform subgroup analysis. We propose two performance measures: the average gain for patients in the future (E) and the probability of recommending a change to a worse treatment at individual patient level (P). Five families of decision rules are applied under different assumptions for the individual treatment effect (TE) variation. Three distributions reflecting optimistic, moderate, and pessimistic scenarios are assumed for true treatment effects across studies. This framework allows us to compare subgroup analyses decision rules, and we demonstrate through simulation studies that there are decision rules for subgroup analysis which can decrease P and increase E simultaneously compared to the situation of no subgroup analysis. These rules are much more liberal than the usual superiority testing. The latter typically implies a dramatic decrease in E.