A Two-Stage Covariate-Adjusted Response-Adaptive Enrichment Design

In the precision medicine paradigm, it is of interest to identify subgroups that benefit most from the treatment. However, the subgroup often cannot be identified until after a large-scale clinical trial. Clinical trials are often designed under the assumption of no treatment-by-covariate interaction effect and enroll all comers. This makes many patients go through unnecessary treatment and may decrease the efficiency of the trial. We propose a two-stage enrichment design that uses covariate-adjusted response-adaptive (CARA) allocation and a novel interaction pseudo-randomization test to evaluate the interaction effect in the interim analysis for binary and continuous outcomes. A pre-defined alpha level is used as the threshold to decide whether a subgroup will be identified and recruited in the second stage. If a below-threshold interaction effect is found, a regression model will be fit and the stratum with the largest treatment effect will be chosen as the best stratum. The trial will continue to the second stage with patients from the best stratum only. If the $p$ -value from the interim analysis is above the threshold, the trial continues with all patients. The primary aim is to test the treatment effect between treatment groups. Different CARA procedures are compared in terms of type I error rates, power, and ethical considerations. The CARA procedure that balances better between efficiency and ethics is used in the proposed two-stage enrichment design.