Distributed Quasi-Poisson regression algorithm for modeling multi-site count outcomes in distributed data networks.

ABSTRACT:

Background

Observational studies incorporating real-world data from multiple institutions facilitate study of rare outcomes or exposures and improve generalizability of results. Due to privacy concerns surrounding patient-level data sharing across institutions, methods for performing regression analyses distributively are desirable. Meta-analysis of institution-specific estimates is commonly used, but has been shown to produce biased estimates in certain settings. While distributed regression methods are increasingly available, methods for analyzing count outcomes are currently limited. Count data in practice are commonly subject to overdispersion, exhibiting greater variability than expected under a given statistical model.

Objective

We propose a novel computational method,

SUBMITTER: Edmondson MJ 

PROVIDER: S-EPMC11874216 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature