AI 中文总结
本研究在拉美HIV流行病学网络中验证了通信高效的联合框架可实现跨国HIV研究的隐私保护分析，其结果与集中分析高度一致且优于传统meta分析，同时指出需解决协调开销等推广问题。
AI 中文摘要
跨国HIV队列研究面临个体参与者数据跨境共享的监管障碍，限制了集中汇总分析。仅交换聚合信息的联合统计方法提供了隐私保护的替代方案，但在HIV研究的真实分布式环境中很少被检验。在此，我们在加勒比、中美洲和南美洲HIV流行病学网络内评估了通信高效的联合框架的可行性和分析性能。我们将抗逆转录病毒疗法启动后的病毒学失败和主要方案变更作为独立结局，针对每个结局估计累积发生率函数（CIFs）并通过基于代理似然的联合实现拟合分层 cause-specific Cox 比例风险模型。每个站点对缺失数据进行插补，开展局部分析，并根据协调计算协议仅共享汇总统计量。联合方法精确再现了集中式 CIFs 并紧密近似集中式 Cox 回归估计值，在两种结局上均优于传统 meta 分析。这些发现表明，真实联合分析对于跨国HIV研究是可行的，可产生与集中式分析高度一致的结果，同时保护数据隐私。不过，事后来自本地分析师的反馈显示，更广泛的采用将需要管理后勤和协调开销，并确保各站点间数据收集和质量控制的协调一致。
英文摘要
Multinational HIV cohort studies face regulatory barriers to cross-border sharing of individual participant data, limiting centralized pooled analyses. Federated statistical methods, which exchange only aggregated information, offer a privacy-preserving alternative but have rarely been examined in real-world distributed environments for HIV research. Here, we evaluate the feasibility and analytical performance of a communication-efficient federated framework within the Caribbean, Central, and South America Network for HIV epidemiology. Virologic failure and major regimen change after antiretroviral therapy initiation were analyzed as separate outcomes; for each, we estimated cumulative incidence functions (CIFs) and fit stratified cause-specific Cox proportional hazards models via a surrogate likelihood-based federated implementation. Each site imputed missing data, conducted local analysis, and shared only summary statistics according to a coordinated computation protocol. The federated approach exactly reproduced centralized CIFs and closely approximated centralized Cox regression estimates, outperforming conventional meta-analysis for both outcomes. These findings demonstrate that authentic federated analysis is feasible for multinational HIV research and can yield results closely aligned with centralized analysis while preserving data privacy. Post-hoc feedback from local analysts, however, indicated that broader adoption will require managing the logistical and coordination overhead and ensuring harmonized data collection and quality control across sites.
