Division of Public Health Sciences, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.
Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Sci Rep. 2022 Apr 22;12(1):6627. doi: 10.1038/s41598-022-09069-0.
We developed a One-shot Distributed Algorithm for Cox proportional-hazards model to analyze Heterogeneous multi-center time-to-event data (ODACH) circumventing the need for sharing patient-level information across sites. This algorithm implements a surrogate likelihood function to approximate the Cox log-partial likelihood function that is stratified by site using patient-level data from a lead site and aggregated information from other sites, allowing the baseline hazard functions and the distribution of covariates to vary across sites. Simulation studies and application to a real-world opioid use disorder study showed that ODACH provides estimates close to the pooled estimator, which analyzes patient-level data directly from all sites via a stratified Cox model. Compared to the estimator from meta-analysis, the inverse variance-weighted average of the site-specific estimates, ODACH estimator demonstrates less susceptibility to bias, especially when the event is rare. ODACH is thus a valuable privacy-preserving and communication-efficient method for analyzing multi-center time-to-event data.
我们开发了一种用于 Cox 比例风险模型的单步分布式算法，用于分析异质多中心生存数据（ODACH），避免了跨站点共享患者级信息的需求。该算法使用来自主导站点的患者级数据和来自其他站点的汇总信息，通过分层实现了替代似然函数来近似 Cox 对数部分似然函数，从而允许各站点的基线风险函数和协变量的分布发生变化。模拟研究和对真实世界阿片类使用障碍研究的应用表明，ODACH 提供的估计值接近通过分层 Cox 模型直接从所有站点分析患者级数据的汇总估计值。与来自荟萃分析的估计值相比，ODACH 估计值对偏倚的敏感性较小，特别是在事件罕见时。因此，ODACH 是一种用于分析多中心生存数据的隐私保护和通信高效的方法。
