ESMARConf2023: {metaBMA} tutorial

Presenter: Daniel Heck
Authors: Heck, Daniel W.
Session: Tutorials 9
Title: A Tutorial on Bayesian Model Averaging for Meta-Analysis Using the metaBMA Package in R
Abstract: The metaBMA package implements Bayesian model-averaging for meta-analysis in R. Whereas fixed-effect meta-analysis assumes a constant, true effect size for all studies, random-effects meta-analysis assumes that true effect sizes vary across studies. Often, the data may not support one of these assumptions unambiguously. As a remedy, Bayesian model averaging combines the results of four meta-analysis models: (1) fixed-effect null hypothesis, (2) fixed-effect alternative hypothesis, (3) random-effects null hypothesis, and (4) random-effects alternative hypothesis. Based on the posterior probabilities of these four models, Bayes factors quantify the evidence for or against two key questions: "Is the overall effect non-zero?" and "Is there between-study variability in effect size?" Besides considering model uncertainty, Bayesian inference enables researchers to include studies sequentially in order to update a meta-analysis as new studies are added to the literature. In this tutorial, I provide a worked example of how to perform a Bayesian, model-averaged meta-analysis in R using the metaBMA package. I also explain how to specify prior distributions and how to interpret posterior model probabilities, Bayes factors, and model-averaged effect-size estimates.
GitHub repository: https://github.com/danheck/metaBMA

Links: https://esmarconf.org/ESMARConf2023Ad...