Integrating real-world data (RWD) from several clinical sites offers great opportunities to improve estimation with a more general population compared to analyses based on a single clinical site. However, sharing patient-level data across sites is practically challenging due to concerns about maintaining patient privacy. We develop a distributed algorithm to integrate heterogeneous RWD from multiple clinical sites without sharing patient-level data. The proposed distributed conditional logistic regression (dCLR) algorithm can effectively account for between-site heterogeneity and requires only one round of communication. Our simulation study and data application with the data of 14,215 COVID-19 patients from 230 clinical sites in the UnitedHealth Group Clinical Research Database demonstrate that the proposed distributed algorithm provides an estimator that is robust to heterogeneity in event rates when efficiently integrating data from multiple clinical sites. Our algorithm is therefore a practical alternative to both meta-analysis and existing distributed algorithms for modeling heterogeneous multi-site binary outcomes.

与基于单个临床站点的分析相比，整合来自多个临床站点的真实世界数据 (RWD) 为改进对更一般人群的估计提供了很好的机会。然而，由于对维护患者隐私的担忧，跨站点共享患者级别的数据实际上具有挑战性。我们开发了一种分布式算法来集成来自多个临床站点的异构 RWD，而无需共享患者级别的数据。所提出的分布式条件逻辑回归（dCLR）算法可以有效地解释站点间的异质性，并且只需要一轮通信。我们对14个数据的模拟研究和数据应用，来自 UnitedHealth Group 临床研究数据库中 230 个临床站点的 215 名 COVID-19 患者证明，当有效整合来自多个临床站点的数据时，所提出的分布式算法提供了一个对事件率异质性具有鲁棒性的估计量。因此，我们的算法是用于建模异构多站点二进制结果的元分析和现有分布式算法的实用替代方案。