隐私保护下具有生存结局的因果元中介分析
Privacy-Preserving Causal Meta-Mediation Analysis with Survival Outcomes
- EPILOGY, Institut Mondor of Biomedical Research, INSERM U955（INSERM U955 EPILOGY 蒙多尔生物医学研究所）
- Université Paris-Est Créteil（巴黎东克雷泰伊大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对多中心生存数据隐私限制，提出联邦因果元中介分析框架，无需共享个体数据即可估计自然间接效应，并开发一步与靶向最大似然估计器，应用于法国健康数据验证。
AI中文摘要：
隐私和数据治理约束常常阻碍跨研究汇总个体水平数据，限制了在多个心场景中应用传统因果中介分析方法。我们提出了一种针对右删失时间至事件结局的联邦因果元中介分析框架，该框架无需共享个体水平数据即可实现协作估计。我们的框架通过结合分布式数据源中的中介变量和结局机制，针对预设人群中的自然间接效应进行估计。逐站点识别策略进一步允许刻画数据源间的异质性，并通过方差分解分离出结局相关、中介相关和交互作用成分。我们开发了联邦一步估计器和靶向最大似然估计器，这些估计器可适应数据自适应和机器学习方法进行干扰函数估计。通过数值模拟评估了所提出估计器的有限样本性能。为展示该框架的实际效用，我们将其应用于法国国家健康数据系统的数据，以评估甲氨蝶呤联合处方在解释TNFi与IL-12/23抑制剂治疗对银屑病患者治疗持续性影响中的作用。
英文摘要：
Privacy and data-governance constraints often prevent pooling individual-level data across studies, limiting the use of conventional approaches for causal media- tion analysis in multicenter settings. We propose a federated causal meta-mediation framework for right-censored time-to-event outcomes that enables collaborative es- timation without sharing individual-level data. Our framework targets natural indirect effects in a prespecified population by combining information on mediator and outcome mechanisms across distributed data sources. A site-by-site identifi- cation strategy further allows heterogeneity across data sources to be character- ized, with a variance decomposition separating outcome-related, mediator-related, and interaction components. We develop federated one-step and targeted maxi- mum likelihood estimators that accommodate data-adaptive and machine-learning methods for nuisance-function estimation. The finite-sample performance of the proposed estimators is evaluated through numerical simulations. To illustrate the practical utility of the framework, we apply it on data from the French National Health Data System to evaluate the role of methotrexate coprescription in explain- ing the effect of TNFi versus IL-12/23 inhibitor therapy on treatment persistence among psoriatic patients.
