用于功效提升的统一自适应富集设计
A Unified Adaptive Enrichment Design for Power Enhancement
浏览论文内容
中文总结 AI 辅助
该研究针对随机对照试验的富集设计缺陷，提出统一自适应框架，通过正则化优化实现平滑入组规则，有效提升功效并降低“赢家诅咒”偏差，为治疗效果估计提供更可靠方法。
中文摘要 AI 辅助
随机对照试验（RCT）是评估治疗效果的金标准，但固定的入组标准和入组决策可能效率低下，尤其是当治疗效果在患者亚群间存在差异时。自适应富集试验利用中期数据更新入组情况以提升效率。富集方法针对两种场景开发：预设亚群，以及由学习到的临界值指导入组的连续协变量。许多富集设计采用不连续规则，偏向单一亚群，若最终估计未考虑依赖数据的入组决策，可能会引发“赢家诅咒”偏差，且需要额外的偏差校正。我们提出了一个统一框架，通过将富集建模为对入组协变量分布的正则化优化来衔接这些场景。在两阶段设计中，第二阶段通过最大化功效目标，同时利用Kullback-Leibler散度项惩罚与预设基线目标人群的偏差，来选择入组混合比例，为“选赢家”规则提供了一种平滑替代方案；该公式可自然扩展至对连续协变量的入组优化。所得估计量是数据自适应入组规则诱导的试验人群中的平均治疗效果，因此不确定性量化除了结局估计外，还必须考虑学习最优入组规则的随机性。我们推导了估计最优入组规则的影响函数表示，并将其纳入最终估计量，得到了明确的渐近方差分解，分解为决策不确定性和结局估计不确定性。模拟结果表明，与传统富集方法相比，该方法提升了功效，同时大幅降低了治疗效果估计中的“赢家诅咒”偏差。
英文摘要
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.
