A Unified Adaptive Enrichment Design for Power Enhancement
提高检验功效的统一自适应富集设计AI 解读 ↓
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提高检验功效的统一自适应富集设计
- 研究问题
- 随机试验根据期中数据调整招募人群时，如何提高功效、减少优胜者偏差，并在最终处理效应推断中计入招募决策的不确定性？
- 方法与思路
- 作者将第二阶段招募分布选择写成带 KL 惩罚的功效优化，统一预设亚组与连续协变量情形，并推导最优招募规则的影响函数。
- 材料陈述的贡献
- 摘要称最终方差可分解为决策与结果估计两类不确定性，模拟显示相较常规富集方法提高功效并明显减少优胜者偏差。
- 阅读关注点
- 应核对随机招募规则诱导的目标人群、优化问题的正则条件、影响函数估计，以及功效、偏差与目标人群变化的共同权衡。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；目标效应随数据驱动招募而变化，摘要未说明完整渐近条件或有限样本保证。
原文摘要
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.