Privacy-protecting estimation of adjusted risk ratios using modified Poisson regression in multi-center studies.

ABSTRACT:

Background

Multi-center studies can generate robust and generalizable evidence, but privacy considerations and legal restrictions often make it challenging or impossible to pool individual-level data across data-contributing sites. With binary outcomes, privacy-protecting distributed algorithms to conduct logistic regression analyses have been developed. However, the risk ratio often provides a more transparent interpretation of the exposure-outcome association than the odds ratio. Modified Poisson regression has been proposed to directly estimate adjusted risk ratios and produce confidence intervals with the correct nominal coverage when individual-level data are available. There are currently no distributed regression algorithms to estimate adjusted risk ratios while avoiding poo

SUBMITTER: Shu D 

PROVIDER: S-EPMC6894462 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature