A Gentle Introduction to Bayesian Network Meta-Analysis Using an Automated R Package

Network meta-analysis is an extension of standard meta-analysis. It allows researchers to build a network of evidence to compare multiple interventions that may have not been compared directly in existing publications. With a Bayesian approach, network meta-analysis can be used to obtain a posterior probability distribution of all the relative treatment effects, which allows for the estimation of relative treatment effects to quantify the uncertainty of parameter estimates, and to rank all the treatments in the network. Ranking treatments using both direct and indirect evidence can provide guidance to policy makers and clinicians for making decisions. The purpose of this paper is to introduce fundamental concepts of Bayesian network meta-analysis (BNMA) to researchers in psychology and social sciences. We discuss several essential concepts of BNMA, including the assumptions of homogeneity and consistency, the fixed and random effects models, prior specification, and model fit evaluation strategies, while pointing out some issues and areas where researchers should use caution in the application of BNMA. Additionally, using an automated R package, we provide a step-by-step demonstration on how to conduct and report the findings of BNMA with a real dataset of psychological interventions extracted from PubMed.