A one-shot, lossless algorithm for cross-cohort learning in mixed-outcomes analysis.

ABSTRACT: In cross-cohort studies, integrating diverse datasets is essential and challenging due to cohort-specific variations, distributed data storage, and privacy concerns. Traditional methods often require data pooling or harmonization, which can reduce efficiency and limit the scope of cross-cohort learning. We introduce mixWAS, a one-shot, lossless algorithm that efficiently integrates distributed electronic health record (EHR) datasets via summary statistics. Unlike existing approaches, mixWAS preserves cohort-specific covariate associations and supports simultaneous mixed-outcome analyses. Simulations demonstrate that mixWAS outperforms conventional methods in accuracy and efficiency across various scenarios. Applied to EHR data from seven cohorts in the US, mixWAS identified 4,530 significa

SUBMITTER: Li R 

PROVIDER: S-EPMC12485519 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature