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dc.contributor.authorTurner, RM
dc.contributor.authorJackson, D
dc.contributor.authorWei, Yinghui
dc.contributor.authorThompson, SG
dc.contributor.authorHiggins, JPT
dc.date.accessioned2016-10-27T21:17:39Z
dc.date.available2016-10-27T21:17:39Z
dc.date.issued2015-03-15
dc.identifier.issn0277-6715
dc.identifier.issn1097-0258
dc.identifier.urihttp://hdl.handle.net/10026.1/6658
dc.description.abstract

<jats:p>Numerous meta‐analyses in healthcare research combine results from only a small number of studies, for which the variance representing between‐study heterogeneity is estimated imprecisely. A Bayesian approach to estimation allows external evidence on the expected magnitude of heterogeneity to be incorporated.</jats:p><jats:p>The aim of this paper is to provide tools that improve the accessibility of Bayesian meta‐analysis. We present two methods for implementing Bayesian meta‐analysis, using numerical integration and importance sampling techniques. Based on 14 886 binary outcome meta‐analyses in the <jats:italic>Cochrane Database of Systematic Reviews</jats:italic>, we derive a novel set of predictive distributions for the degree of heterogeneity expected in 80 settings depending on the outcomes assessed and comparisons made. These can be used as prior distributions for heterogeneity in future meta‐analyses.</jats:p><jats:p>The two methods are implemented in R, for which code is provided. Both methods produce equivalent results to standard but more complex Markov chain Monte Carlo approaches. The priors are derived as log‐normal distributions for the between‐study variance, applicable to meta‐analyses of binary outcomes on the log odds‐ratio scale. The methods are applied to two example meta‐analyses, incorporating the relevant predictive distributions as prior distributions for between‐study heterogeneity.</jats:p><jats:p>We have provided resources to facilitate Bayesian meta‐analysis, in a form accessible to applied researchers, which allow relevant prior information on the degree of heterogeneity to be incorporated. © 2014 The Authors. <jats:italic>Statistics in Medicine</jats:italic> published by John Wiley &amp; Sons Ltd.</jats:p>

dc.format.extent984-998
dc.format.mediumPrint-Electronic
dc.languageen
dc.language.isoeng
dc.publisherWiley
dc.subjectmeta-analysis
dc.subjectBayesian methods
dc.subjectheterogeneity
dc.subjectprior distributions
dc.titlePredictive distributions for between‐study heterogeneity and simple methods for their application in Bayesian meta‐analysis
dc.typejournal-article
dc.typeJournal Article
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000351096900006&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.issue6
plymouth.volume34
plymouth.publication-statusPublished
plymouth.journalStatistics in Medicine
dc.identifier.doi10.1002/sim.6381
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/EXTENDED UoA 10 - Mathematical Sciences
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA10 Mathematical Sciences
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
plymouth.organisational-group/Plymouth/Users by role/Researchers in ResearchFish submission
dc.publisher.placeEngland
dcterms.dateAccepted2014-11-12
dc.identifier.eissn1097-0258
dc.rights.embargoperiodNot known
rioxxterms.versionofrecord10.1002/sim.6381
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2015-03-15
rioxxterms.typeJournal Article/Review
plymouth.oa-locationhttp://onlinelibrary.wiley.com/doi/10.1002/sim.6381/abstract


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