混合对照试验的功效与样本量计算
Power and Sample Size Calculations for Hybrid Controlled Trials
AI总结：
本文针对混合对照试验样本量确定的难题，提出含5个常规RCT参数与3个EC可比性参数的5+3设计，推导估计量渐近分布并实现连续/二分类结局的样本量计算，相关方法已封装为hctdesign R包。
AI中文摘要：
混合对照试验（HCTs）通过外部对照（ECs）扩充随机对照（RCTs），以应对RCT面临的实际挑战，并提升罕见病、肿瘤学、儿科学等场景的统计功效。然而，前瞻性样本量确定颇具难度，因为所需的RCT样本量取决于ECs与RCT对照的可比性，而该信息在试验规划阶段无法获取。本文提出一种用于HCT样本量确定的5+3设计，基于平均处理效应的逆概率加权估计量构建，该框架采用5个常规RCT设计参数，以及3个描述EC可比性的标量参数：无结局漂移的EC数量、RCT与EC协变量分布的重叠系数、抽样机制与对照潜在结局关联的相关系数。本文推导了该估计量的渐近分布，并证明在所提出的工作模型下其方差由这些设计参数决定，进而可针对连续型与二分类结局开展样本量计算。模拟研究评估了有限样本表现，一项真实临床应用示例说明了其实际用途，该方法已在hctdesign R包中实现。
英文摘要：
Hybrid controlled trials (HCTs) augment randomized controls with external controls (ECs) to address practical challenges in randomized controlled trials (RCTs) and improve statistical power in settings such as rare diseases, oncology, and pediatrics. However, prospective sample-size determination is challenging because the required RCT sample size depends on the comparability of ECs with RCT controls, which are unavailable at the planning stage. We propose a 5+3 design for HCT sample-size determination based on an inverse probability weighting estimator of the average treatment effect. The framework uses five conventional RCT design parameters and three additional scalar parameters characterizing EC comparability: the number of outcome-drift-free ECs, an overlap coefficient for the covariate distributions of the RCT and ECs, and a correlation coefficient linking the sampling mechanism to the control potential outcome. We establish the asymptotic distribution of the estimator and prove that its variance is determined by these design parameters under the proposed working models, yielding sample-size calculations for both continuous and binary outcomes. Simulation studies evaluate finite-sample performance, and a real clinical application illustrates its practical use. The method is implemented in the hctdesign R package.
