How to read and interpret the results of a Bayesian network meta-analysis: a short tutorial O'Connor, Annette Hu, Dapeng O’Connor, Annette Wang, Chong Winder, Charlotte Sargeant, Jan Wang, Chong
dc.contributor.department Statistics
dc.contributor.department Veterinary Diagnostic and Production Animal Medicine 2020-02-28T16:08:09.000 2020-07-07T05:13:06Z 2020-07-07T05:13:06Z Wed Jan 01 00:00:00 UTC 2020 2019-12-01
dc.description.abstract <p>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.</p>
dc.description.comments <p>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." <em>Animal Health Research Reviews</em> 20, no. 2 (2019): 106-115. DOI: <a href="" target="_blank">10.1017/S1466252319000343</a>. Posted with permission.</p>
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dc.identifier.articleid 1187
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dc.language.iso en
dc.source.bitstream archive/|||Fri Jan 14 21:29:17 UTC 2022
dc.source.uri 10.1017/S1466252319000343
dc.subject.disciplines Multivariate Analysis
dc.subject.disciplines Veterinary Medicine
dc.subject.keywords Network meta-analysis
dc.subject.keywords systematic review
dc.subject.keywords tutorial
dc.subject.keywords veterinary science
dc.title How to read and interpret the results of a Bayesian network meta-analysis: a short tutorial
dc.type article
dc.type.genre article
dspace.entity.type Publication
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