Distributed Regression: Estimation, Prediction, and Inference

• Activity Number: 43 - Distributed Regressions in Real-World Data
• Type: Invited
• Date/Time: Sunday, August 8, 2021 : 3:30 PM to 5:20 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Yong Chen*
• Companies: University of Pennsylvania

Abstract

In evidence synthesis from different electronic health records (EHRs) datasets, the feature of privacy-preserving is important as patient-level data are often protected against sharing across clinical sites. Conventional meta-analysis might suffer from substantial bias when studying rare conditions due to the limited number of events in single clinical sites. In this talk, I will introduce distributed algorithms for commonly used regression models to study binary and time-to-event outcomes, in the presence of between-site heterogeneity. The algorithms are built to be communication-efficient in the sense that they require no iterative communication across clinical sites. We will also demonstrate novel tools for prediction under distributed research network setting. We will show the applicability of our methods using real-world data.