AI 中文总结
针对跨机构中介分析受隐私限制问题，开发隐私保护联合中介框架，结合可再生学习与反事实因果中介分析，仅用低维统计协作研究治疗机制，经模拟和实际应用验证效果，应用于患者护理网络揭示BMI介导作用占比小。
AI 中文摘要
电子健康记录（EHR）网络为大规模研究治疗机制提供了前所未有的机会，但跨机构的中介分析常受隐私和治理限制阻碍，限制了患者级数据共享。我们开发了一个隐私保护的联合中介框架，无需在参与站点间交换个体记录就能估计自然直接和间接效应。该方法将可再生学习与反事实因果中介分析相结合，机构仅用低维汇总统计就能协作研究治疗机制。模拟研究和实际应用表明，联合估计器在保护患者隐私的同时能紧密重现汇总数据结果。我们将该方法应用于印第安纳患者护理网络的32146名患者，评估体重指数（BMI）对GLP-1受体激动剂降低糖化血红蛋白（HbA1c）效果的中介作用程度。BMI介导的途径在总体治疗效果中占比小，表明多数血糖改善通过体重减轻以外的机制实现。
英文摘要
Electronic health record (EHR) networks provide unprecedented opportunities to study treatment mechanisms at scale, but mediation analyses across institutions are often hindered by privacy and governance constraints that restrict sharing of patient-level data. We developed a privacy-preserving federated mediation framework that enables estimation of natural direct and indirect effects without exchanging individual-level records across participating sites. The proposed approach integrates renewable learning with counterfactual causal mediation analysis, allowing institutions to collaboratively investigate treatment mechanisms using only low-dimensional summary statistics. Both simulation studies and the real-world application demonstrated that the federated estimator closely reproduced pooled-data results while preserving patient privacy. We applied the method to 32,146 patients in the Indiana Network for Patient Care to evaluate the extent to which body mass index (BMI) mediates the effect of GLP-1 receptor agonist on glycated hemoglobin (HbA1c) reduction. The BMI-mediated pathway accounted for only a small proportion of the overall treatment effect, suggesting that most glycemic improvement occurred through mechanisms other than weight loss.
