The randomized controlled trial (RCT) is the gold standard for estimating the average treatment effect (ATE) of a medical intervention but requires 100s-1000s of subjects, making it expensive and difficult to implement. While a cross-over trial can reduce sample size requirements by measuring the treatment effect per individual, it is only applicable to chronic conditions and interventions whose effects dissipate rapidly. Another approach is to replace or augment data collected from an RCT with external data from prospective studies or prior RCTs, but it is vulnerable to confounders in the external or augmented data. We propose to simulate the cross-over trial to overcome its practical limitations while exploiting its strengths. We propose a novel framework, SECRETS, which, for the first time, estimates the individual treatment effect (ITE) per patient in the RCT study without using any external data by leveraging a state-of-the-art counterfactual estimation algorithm, called synthetic intervention. It also uses a new hypothesis testing strategy to determine whether the treatment has a clinically significant ATE based on the estimated ITEs. We show that SECRETS can improve the power of an RCT while maintaining comparable significance levels; in particular, on three real-world clinical RCTs (Phase-3 trials), SECRETS increases power over the baseline method by $\boldsymbol{6}$-$\boldsymbol{54\%}$ (average: 21.5%, standard deviation: 15.8%).

翻译：随机对照试验（RCT）是评估医疗干预平均处理效应（ATE）的金标准，但需招募数百至数千名受试者，导致实施成本高昂且难度较大。虽然交叉试验可通过测量个体处理效应减少样本量需求，但仅适用于干预效果快速消退的慢性疾病。另一种方法是用前瞻性研究或既往RCT的外部数据替代或增强RCT中收集的数据，但该方法易受外部或增强数据中混杂因素的影响。我们提出通过模拟交叉试验来克服其实际局限性，同时发挥其优势，并首次提出名为SECRETS的新框架：该框架无需使用任何外部数据，仅通过利用最先进的反事实估计算法——合成干预——来估计RCT研究中每位患者的个体处理效应（ITE），并采用新的假设检验策略基于估计的ITE判定干预是否具有临床意义的平均处理效应。研究表明，SECRETS能在保持可比显著性水平的同时提升RCT的统计效能；具体而言，在三项真实临床RCT（Ⅲ期试验）中，SECRETS相较于基线方法将统计效能提升了$\boldsymbol{6}$-$\boldsymbol{54\%}$（均值：21.5%，标准差：15.8%）。