Covariate-Adaptive Sample Size Re-estimation for Population-Standardized Historical Control Designs in Single-Arm Trials
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AI 根据所提供的论文材料归类：摘要以实际入组人群的标准化治疗效应为目标，并处理外部历史对照的基线不平衡。
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单臂试验总体标准化历史对照设计的协变量自适应样本量重估
- 研究问题
- 当实际入组人群与历史对照或计划人群的基线分布不同，如何在不查看治疗组结局的情况下调整样本量并维持目标功效？
- 方法与思路
- 作者以实际入组人群定义标准化效应，通过预设平衡评分标准化历史对照，并依据累积基线协变量和停止规则更新样本量。
- 材料陈述的贡献
- 摘要给出条件与无条件功效解释及近似错误率控制的充分条件，并称分布偏移模拟中自适应设计比固定设计更接近目标功效。
- 阅读关注点
- 应核对历史对照可比性、平衡评分与标准化条件、随入组变化的估计目标，以及重复样本量重估和停止规则下的错误率论证。
- 阅读边界
- 这是 arXiv 预印本，同行评审与接收未经核实；摘要未给出充分条件细节或功效数值，结局盲态不能自动消除历史对照混杂。
原文摘要
Externally controlled single-arm trials are increasingly considered when randomized controls are infeasible, but baseline imbalance between the active-arm trial and historical controls complicates both estimation and sample size planning. We propose a population-standardized design framework in which the target estimand is defined for the actually enrolled active-arm population and historical-control outcomes are standardized to that population through a pre-specified balancing score. Building on this estimand, we develop an outcome-blinded, covariate-adaptive sample size re-estimation (SSR) procedure that updates the required sample size using only accumulating baseline covariates, without using active-arm outcomes during enrollment. The method combines an initial scenario-based design with sequential updates of the enrolled-population score distribution, standardized control parameters, and target sample size under pre-specified stopping rules. We give conditional and unconditional power interpretations and sufficient conditions for approximate type I error control under repeated blinded SSR. In simulation studies with distributional shifts between planned and true active-arm populations, fixed designs based only on planning assumptions lost power, whereas the proposed SSR maintained power near the target level and performed similarly to an oracle design. In an illustrative ADCS-based example, the proposed procedures yielded different final sample sizes across adjustment sets, reflecting evolving enrolled-population covariate profiles. These results support covariate-adaptive, outcome-blinded SSR as a practical design strategy for externally controlled single-arm trials that target population-standardized treatment effects.