用外部数据提升随机试验中个体治疗效果的估计精度
Robust estimation of heterogeneous treatment effects in randomized trials leveraging external data
- 引入QR-learner模型，融合外部数据估计条件平均治疗效应
- 外部数据不匹配时仍能准确恢复真实治疗效应，误差更低
- 适合需要个性化医疗决策的研究者和临床试验设计者
随机试验通常旨在检测平均治疗效应，但往往缺乏统计功效来揭示个体层面的治疗效果异质性，限制了其在个性化决策中的价值。为解决此问题，我们提出QR-learner——一种模型无关的学习方法，通过利用其他试验或观察性研究的外部数据，在试验人群中估计条件平均治疗效应（CATE）。该方法具有鲁棒性：相较于仅依赖试验数据的CATE学习器，可降低均方误差；即使外部数据与试验人群不一致，也能保证恢复真实CATE。此外，我们提出一种结合QR-learner与试验独有CATE学习器的程序，其渐近均方误差不低于任一组件，且可能更优。我们在模拟研究中评估了该方法性能，并应用于真实数据集，结果表明在CATE估计和检测异质效应的统计功效上均有提升。
原文摘要 · Abstract (English)
Randomized trials are typically designed to detect average treatment effects but often lack the statistical power to uncover individual-level treatment effect heterogeneity, limiting their value for personalized decision-making. To address this, we propose the QR-learner, a model-agnostic learner that estimates conditional average treatment effects (CATE) within the trial population by leveraging external data from other trials or observational studies. The proposed method is robust: it can reduce the mean squared error relative to a trial-only CATE learner, and is guaranteed to recover the true CATE even when the external data are not aligned with the trial. Moreover, we introduce a procedure that combines the QR-learner with a trial-only CATE learner and show that it asymptotically matches or exceeds both component learners in terms of mean squared error. We examine the performance of our approach in simulation studies and apply the methods to a real-world dataset, demonstrating improvements in both CATE estimation and statistical power for detecting heterogeneous effects.
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