提出无需中心服务器的分布式生存分析方法，实现跨医院隐私保护下的风险因子评估。
Communication-efficient distributed hazard difference estimation for heterogeneous multi-site survival data
- 基于加性风险模型的非迭代算法，仅需汇总统计量协作，无需共享原始数据。
- 在47,778例急诊患者数据上准确估计危险度差异，检测到单个机构无法发现的风险因子。
- 适合医疗多中心合作场景，尤其适用于隐私受限环境下的生存分析研究。
多中心合作可构建单个医院无法完成的生存预测模型，但隐私法规和受保护计算环境禁止患者级数据共享及持续的中心化连接。我们提出DiSAH（分布式生存分析加性风险模型），一种无需专用中心服务器的联邦算法，其闭式非迭代结构避免了反复通信。协作仅需汇总统计量聚合，任何机构均可执行，且不涉及患者级数据外传。DiSAH是首个能估计危险度差异（即每个风险因子导致事件率绝对变化）的联邦方法，为分诊、资源配置和卫生经济学评估提供可操作指标。在模拟实验及美国与新加坡47,778名急诊患者数据中，DiSAH在准确性和区分度上与集中式分析相当，恢复出单个机构无能力检测的死亡风险因子，优于元分析和本地模型。
原文摘要 · Abstract (English)
Multi-site collaboration can power survival models that no single hospital could fit alone, but privacy rules and protected computing environments block patient-level data sharing and the persistent server connections required by iterative federated methods. We present DiSAH (\underline{\textbf{Di}}stributed \underline{\textbf{S}}urvival via \underline{\textbf{A}}dditive \underline{\textbf{H}}azards), a federated algorithm for time-to-event analysis whose closed-form, non-iterative structure removes the need for a dedicated central server. Coordination requires only aggregation of summary statistics, which any site can perform, with no patient-level data leaving the site. DiSAH is the first federated method to estimate hazard differences, the absolute change in event rate attributable to each risk factor, providing an actionable scale for triage, resource allocation, and health-economic evaluation. Across simulations and 47,778 emergency-department patients from the United States and Singapore, DiSAH matches centralized analysis in accuracy and discrimination, recovers mortality risk factors no individual site was powered to detect, and outperforms meta-analysis and local models.
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