An efficient and accurate distributed learning algorithm for modeling multi-site zero-inflated count outcomes.

ABSTRACT: Clinical research networks (CRNs), made up of multiple healthcare systems each with patient data from several care sites, are beneficial for studying rare outcomes and increasing generalizability of results. While CRNs encourage sharing aggregate data across healthcare systems, individual systems within CRNs often cannot share patient-level data due to privacy regulations, prohibiting multi-site regression which requires an analyst to access all individual patient data pooled together. Meta-analysis is commonly used to model data stored at multiple institutions within a CRN but can result in biased estimation, most notably in rare-event contexts. We present a communication-efficient, privacy-preserving algorithm for modeling multi-site zero-inflated count outcomes within a CRN. Our method,

SUBMITTER: Edmondson MJ 

PROVIDER: S-EPMC8490431 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature