Bayesian Methods for Meta-Analyses of Binary Outcomes: Implementations, Examples, and Impact of Priors.

ABSTRACT: Bayesian methods are an important set of tools for performing meta-analyses. They avoid some potentially unrealistic assumptions that are required by conventional frequentist methods. More importantly, meta-analysts can incorporate prior information from many sources, including experts' opinions and prior meta-analyses. Nevertheless, Bayesian methods are used less frequently than conventional frequentist methods, primarily because of the need for nontrivial statistical coding, while frequentist approaches can be implemented via many user-friendly software packages. This article aims at providing a practical review of implementations for Bayesian meta-analyses with various prior distributions. We present Bayesian methods for meta-analyses with the focus on odds ratio for binary outcomes. We

SUBMITTER: Al Amer FM 

PROVIDER: S-EPMC8036799 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature