标题： 面向数据融合的因果生存分析在分布偏移下的目标 显示英文标题

标题： Targeted Data Fusion for Causal Survival Analysis Under Distribution Shift

摘要： 多数据源中的因果推断为增强科学发现的普适性和可重复性提供了有前景的途径。然而，针对时间至事件结果的数据整合方法，在生物医学研究中很常见，但发展不足。现有的方法侧重于二元或连续结果，但未能解决生存分析的独特挑战，如删失和离散时间与连续时间的整合。为弥合这一差距，我们提出了两种新方法来估计多源设置下的目标地点特定因果效应。首先，我们开发了一种半参数有效估计器，适用于个体层面数据可以在站点间共享的情况。其次，我们引入了一种联邦学习框架，设计用于隐私受限环境，该框架动态重新加权来源特定贡献以考虑与目标人群的差异。这两种方法利用灵活的非参数机器学习模型来提高稳健性和效率。我们通过模拟研究和一项在美国、巴西、秘鲁和瑞士的跨性别男性和顺性别男性以及在撒哈拉以南非洲女性中开展的单克隆中和抗体预防HIV-1的多站点随机试验的应用，展示了我们方法的效用。我们的研究结果强调了这些方法在分布变化下实现高效且保护隐私的时间至事件结果因果推断的潜力。 显示英文摘要

摘要： Causal inference across multiple data sources offers a promising avenue to enhance the generalizability and replicability of scientific findings. However, data integration methods for time-to-event outcomes, common in biomedical research, are underdeveloped. Existing approaches focus on binary or continuous outcomes but fail to address the unique challenges of survival analysis, such as censoring and the integration of discrete and continuous time. To bridge this gap, we propose two novel methods for estimating target site-specific causal effects in multi-source settings. First, we develop a semiparametric efficient estimator for settings where individual-level data can be shared across sites. Second, we introduce a federated learning framework designed for privacy-constrained environments, which dynamically reweights source-specific contributions to account for discrepancies with the target population. Both methods leverage flexible, nonparametric machine learning models to improve robustness and efficiency. We illustrate the utility of our approaches through simulation studies and an application to multi-site randomized trials of monoclonal neutralizing antibodies for HIV-1 prevention, conducted among cisgender men and transgender persons in the United States, Brazil, Peru, and Switzerland, as well as among women in sub-Saharan Africa. Our findings underscore the potential of these methods to enable efficient, privacy-preserving causal inference for time-to-event outcomes under distribution shift.