Simple subgroup approximations to optimal treatment regimes from randomized clinical trial data

We consider the use of randomized clinical trial (RCT) data to identify simple treatment regimes based on some subset of the covariate space, A. The optimal subset, (A) over cap, is selected by maximizing the expected outcome under a treat-if-in-A regime, and is restricted to be a simple, as it is desirable that treatment decisions be made with only a limited amount of patient information required. We consider a two-stage procedure. In stage 1, non-parametric regression is used to estimate treatment effects for each subject, and in stage 2 these treatment effect estimates are used to systematically evaluate many subgroups of a simple, prespecified form to identify (A) over cap. The proposed methods were found to perform favorably compared with two existing methods in simulations, and were applied to prehypertension data from an RCT.