How to read and interpret the results of a Bayesian network meta-analysis: a short tutorial

Date
2019-12-01
Authors
O'Connor, Annette
Hu, Dapeng
O’Connor, Annette
Wang, Chong
Winder, Charlotte
Sargeant, Jan
Wang, Chong
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StatisticsVeterinary Diagnostic and Production Animal Medicine
Abstract

In this manuscript we use realistic data to conduct a network meta-analysis using a Bayesian approach to analysis. The purpose of this manuscript is to explain, in lay terms, how to interpret the output of such an analysis. Many readers are familiar with the forest plot as an approach to presenting the results of a pairwise meta-analysis. However when presented with the results of network meta-analysis, which often does not include the forest plot, the output and results can be difficult to understand. Further, one of the advantages of Bayesian network meta-analyses is in the novel outputs such as treatment rankings and the probability distributions are more commonly presented for network meta-analysis. Our goal here is to provide a tutorial for how to read the outcome of network meta-analysis rather than how to conduct or assess the risk of bias in a network meta-analysis.

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This article is published as Hu, D., A. M. O'Connor, C. B. Winder, J. M. Sargeant, and C. Wang. "How to read and interpret the results of a Bayesian network meta-analysis: a short tutorial." Animal Health Research Reviews 20, no. 2 (2019): 106-115. DOI: 10.1017/S1466252319000343. Posted with permission.

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