后验漂移下的试验证据迁移及可能的隐藏混杂
Transporting Trial Evidence Under Posterior Drift and Possible Hidden Confounding
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中文总结 AI 辅助
针对随机试验证据无法代表目标人群、观察性研究存在隐藏混杂的问题，提出鲁棒后验漂移框架，结合随机试验锚点与观察性研究信息，通过极小极大准则估计目标人群平均治疗效果，模拟及应用验证其更具优势。
中文摘要 AI 辅助
随机试验能提供内部有效的治疗效果证据，但试验参与者可能无法代表目标人群；而观察性研究通常更接近目标人群，但其治疗分配可能受可能的隐藏混杂影响。当精确条件效应迁移性可能失效时，我们开发了一种鲁棒后验漂移框架，用于估计观察性目标人群的平均治疗效果。该框架将观察性条件潜在结果回归表示为其随机试验对应项加上源特异性漂移；随机试验作为内部有效的锚点，观察性研究则提供目标协变量分布和目标因果对比的部分信息。为考虑可能的隐藏混杂，我们考虑了由广义倾向评分上的敏感性参数诱导的Rosenbaum型不确定集，并通过极小极大最坏情况风险准则估计漂移。我们推导了辅助制度下的效率结果，建立了极小极大估计器的均匀收敛性和近最优性保证，并处理了一般参数化和平滑非参数化漂移类。模拟研究及ACTG 175--WIHS应用表明，与精确迁移性分析相比，所提分析能产生更谨慎、可解释的目标人群效应估计值。
英文摘要
Randomized trials provide internally valid treatment-effect evidence, but trial participants may not represent the target population. In contrast, observational studies are often closer to the target population, but their treatment assignment may be affected by possible hidden confounding. We develop a robust posterior-drift framework for estimating the average treatment effect in an observational target population when exact conditional-effect transportability may fail. The framework represents observational conditional potential-outcome regressions as their randomized-trial counterparts plus source-specific drifts. The randomized trial serves as an internally valid anchor, while the observational study supplies the target covariate distribution and partial information about the target causal contrast. To account for possible hidden confounding, we consider a Rosenbaum-type uncertainty set induced by a sensitivity parameter on the generalized propensity score and estimate the drift through a minimax worst-case risk criterion. We derive efficiency results in auxiliary regimes, establish uniform concentration and near-optimality guarantees for the minimax estimator, and handle general parametric and smooth nonparametric drift classes. Simulations and an ACTG 175--WIHS application show that the proposed analysis yields more cautious and interpretable target-population effect estimates than exact-transportability analyses.
