A Unified Adaptive Enrichment Design for Power Enhancement
Randomized controlled trials (RCTs) are the gold standard for evaluating treatment effects, but fixed eligibility criteria and enrollment decisions can be inefficient, especially when treatment effects vary across patient subpopulations. Adaptive enrichment trials update enrollment using interim data to improve efficiency. Enrichment methods are developed for two settings: prespecified subgroups, and continuous covariates where enrollment is guided by a learned cutoff. Many enrichment designs adopt discontinuous rules that favor one single subgroup, which may induce "winner's curse" bias if final estimation does not account for the data-dependent enrollment decision and require additional bias correction. We propose a unified framework that bridges these settings by formulating enrichment as a regularized optimization over the enrolled covariate distribution. In a two-stage design, Stage 2 selects an enrollment mixture by maximizing a power objective while penalizing deviation from a prespecified baseline target population through a Kullback-Leibler divergence term, providing a smooth alternative to pick-the-winner rules; the same formulation extends naturally to optimizing enrollment over continuous covariates. The resulting estimand is the average treatment effect in the trial population induced by the data-adaptive enrollment rule, so uncertainty quantification must account for randomness in learning the optimal enrollment rule, in addition to outcome estimation. We derive an influence-function representation for the estimated optimal enrollment rule and account for it in the final estimator, yielding an explicit asymptotic variance decomposition into decision uncertainty and outcome-estimation uncertainty. Simulations demonstrate improved power relative to conventional enrichment approaches while substantially reducing winner's curse bias in treatment effect estimation.