DMS Statistics and Data Science Seminar

Speaker: Dr. Rui Duan ( Assistant Professor of Biostatistics, Harvard T.H. Chan School of Public Health)

Title: Collaborative Multi-Site Statistical Inference

Abstract: In response to the increasing demand for generating real-world evidence from multi-site collaborative studies, we introduce novel collaborative learning approaches designed to estimate the average treatment effect in multi-site settings with data-sharing constraints. Our methods operate in a federated manner, utilizing individual-level data from a user-defined target population while leveraging summary statistics from other source populations to construct efficient estimators for the average treatment effect on the target population. Crucially, our federated approach eliminates the need for iterative communications between sites, making it particularly well-suited for research consortia that lack the resources to implement automated data-sharing infrastructures.

Compared to existing data integration methods in causal inference, our approach accommodates distributional shifts in outcomes, treatments, and baseline covariates. Additionally, under the right conditions, it achieves the semiparametric efficiency bound. We conduct simulation studies to demonstrate the efficiency gains obtained by incorporating data from multiple sources, as well as the robustness of our methods in the face of varying levels of distributional shifts. Furthermore, we showcase several real-world case studies that estimate treatment effectiveness using data from multiple institutions.