Problem
Research questions and friction points this paper is trying to address.
Hybrid controlled trials
Sample size determination
External controls
Power and Sample Size Calculations for Hybrid Controlled Trials
🤖 AI Summary
该研究提出了一种5+3设计方法，通过逆概率加权估计平均治疗效应来解决混合对照试验中样本量确定的问题。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Hybrid Controlled Trials
Inverse Probability Weighting Estimator
Sample Size Determination
External Controls Comparability
hctdesign R Package
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