发表机构
Wakayama Medical University; Chuo University（和歌山医科大学; 中央大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对单臂试验中试验组与历史对照基线不平衡问题，提出群体标准化设计框架及协变量自适应样本量重新估计程序，该程序结合初始设计与顺序更新，模拟及示例结果显示其能维持功效，是实用设计策略。
AI 中文摘要
当随机对照不可行时，外部对照单臂试验越来越受到关注，但试验组与历史对照之间的基线不平衡使估计和样本量规划变得复杂。我们提出了一个群体标准化设计框架，通过预先指定的平衡分数将历史对照结果标准化到实际入组的试验组人群。在此基础上，我们开发了一种结果盲法、协变量自适应样本量重新估计（SSR）程序，该程序仅使用累积的基线协变量来更新所需样本量，在入组期间不使用试验组结果。该方法将基于初始场景的设计与根据预先指定的停止规则对入组人群分数分布、标准化对照参数和目标样本量的顺序更新相结合。我们给出了重复盲法SSR下近似I型错误控制的条件和无条件功效解释以及充分条件。在模拟研究中，仅基于规划假设的固定设计失去了功效，而所提出的SSR将功效维持在目标水平附近，并且表现与理想设计相似。在一个基于ADCS的示例中，所提出的程序在不同调整集下产生了不同的最终样本量，反映了入组人群协变量概况的演变。这些结果支持协变量自适应、结果盲法SSR作为针对群体标准化治疗效果的外部对照单臂试验的一种实用设计策略。
英文摘要
Externally controlled single-arm trials provide an option when limited patient populations or ethical constraints make concurrent randomized controls impractical, including in rare diseases and investigator-initiated trials with limited recruitment. Standardization improves comparability by aligning external controls with the enrolled population, but does not by itself preserve planned power. Differences between anticipated and enrolled covariate distributions can change the precision of the standardized control estimate and leave a fixed-size trial underpowered. We propose an outcome-blinded, covariate-adaptive sample size re-estimation procedure that translates these changes in precision into updated recruitment targets. Historical-control data and accumulating active-arm baseline covariates are used to update external-control standardization and its estimated precision. Adaptation requires no active-arm outcomes and retains the prespecified clinically meaningful effect to be detected. We state sufficient conditions for type I error calibration and power after adaptive stopping, assess operating characteristics through simulations, and illustrate implementation using Alzheimer's Disease Cooperative Study data. In the primary simulations, adaptive recruitment restored power lost under standardized fixed designs, achieving at least the target power across the distribution-shift settings and outcome models examined. Type I error rates were close to nominal, although mild inflation remained with a smaller historical-control sample. The application illustrated how accumulating baseline information guided recruitment revisions. By adapting sample size to the precision of the population-standardized control estimate, the proposed procedure addresses a source of power loss that standardization alone does not resolve.
Comments28 pages, 9 tables, 1 figures
