Distributed learning for heterogeneous clinical data with application to integrating COVID-19 data across 230 sites.

ABSTRACT: 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 demonstrat

SUBMITTER: Tong J 

PROVIDER: S-EPMC9198031 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature