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. 2020 May 19:7:271.
doi: 10.3389/fvets.2020.00271. eCollection 2020.

How to Conduct a Bayesian Network Meta-Analysis

Dapeng Hu  1 , Annette M O'Connor  2 , Chong Wang  1   3 , Jan M Sargeant  4 , Charlotte B Winder  4
Affiliations

How to Conduct a Bayesian Network Meta-Analysis

Dapeng Hu et al. Front Vet Sci. .

Abstract

Network meta-analysis is a general approach to integrate the results of multiple studies in which multiple treatments are compared, often in a pairwise manner. In this tutorial, we illustrate the procedures for conducting a network meta-analysis for binary outcomes data in the Bayesian framework using example data. Our goal is to describe the workflow of such an analysis and to explain how to generate informative results such as ranking plots and treatment risk posterior distribution plots. The R code used to conduct a network meta-analysis in the Bayesian setting is provided at GitHub.

Keywords: Bayesian; network meta-analysis; systematic review; tutorial; veterinary science.

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Figures

Figure 1
Figure 1
An example of the formatting of the BUGS code for the comparative model. This code was modified from code originally published elsewhere (7).
Figure 2
Figure 2
An example of the formatting of the BUGS code for the baseline effects model. This code was modified from code originally published elsewhere (7).
Figure 3
Figure 3
The ranking plot. The left column is the treatment name with the number of studies including that treatment. The right column is the posterior mean ranking of the absolute risk of each treatment and 95% credible interval. Lower rankings have lower incidence of the disease.
Figure 4
Figure 4
The network plot. Each node represents treatment and the number is the corresponding number of studies including that treatment. An edge between two nodes (treatments) means there were studies comparing these two treatments.
Figure 5
Figure 5
The posterior distribution of the event risk of each treatment.
See this image and copyright information in PMC

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