A One-Shot Lossless Algorithm for Cross-Cohort Learning in Mixed-Outcomes Analysis.

ABSTRACT: In cross-cohort studies, integrating diverse datasets, such as electronic health records (EHRs), is both essential and challenging due to cohort-specific variations, distributed data storage, and data privacy concerns. Traditional methods often require data pooling or complex data 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 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 th

SUBMITTER: Li R 

PROVIDER: S-EPMC10802662 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature