Bayesian Meta-Analysis for Binary Data and Prior Distribution on Models.

ABSTRACT: In meta-analysis, the structure of the between-sample heterogeneity plays a crucial role in estimating the meta-parameter. A Bayesian meta-analysis for binary data has recently been proposed that measures this heterogeneity by clustering the samples and then determining the posterior probability of the cluster models through model selection. The meta-parameter is then estimated using Bayesian model averaging techniques. Although an objective Bayesian meta-analysis is proposed for each type of heterogeneity, we concentrate the attention of this paper on priors over the models. We consider four alternative priors which are motivated by reasonable but different assumptions. A frequentist validation with simulated data has been carried out to analyze the properties of each prior distribution f

SUBMITTER: Negrin-Hernandez MA 

PROVIDER: S-EPMC7832911 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature