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dc.contributor.authorTurner, RMen
dc.contributor.authorJackson, Den
dc.contributor.authorWei, Yen
dc.contributor.authorThompson, SGen
dc.contributor.authorHiggins, JPTen
dc.date.accessioned2016-10-27T21:17:39Z
dc.date.available2016-10-27T21:17:39Z
dc.date.issued2015-03-15en
dc.identifier.urihttp://hdl.handle.net/10026.1/6658
dc.description.abstract

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. 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 Cochrane Database of Systematic Reviews, 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. 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. 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.

en
dc.format.extent984 - 998en
dc.languageengen
dc.language.isoengen
dc.subjectBayesian methodsen
dc.subjectheterogeneityen
dc.subjectmeta-analysisen
dc.subjectprior distributionsen
dc.subjectBayes Theoremen
dc.subjectData Interpretation, Statisticalen
dc.subjectDatabases, Bibliographicen
dc.subjectHumansen
dc.subjectLogistic Modelsen
dc.subjectMeta-Analysis as Topicen
dc.subjectReview Literature as Topicen
dc.titlePredictive distributions for between-study heterogeneity and simple methods for their application in Bayesian meta-analysis.en
dc.typeJournal Article
plymouth.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/25475839en
plymouth.issue6en
plymouth.volume34en
plymouth.publication-statusPublisheden
plymouth.journalStat Meden
dc.identifier.doi10.1002/sim.6381en
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/00 Groups by role
plymouth.organisational-group/Plymouth/00 Groups by role/Academics
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Computing, Electronics and Mathematics
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA10 Mathematical Sciences
dc.publisher.placeEnglanden
dcterms.dateAccepted2014-11-12en
dc.identifier.eissn1097-0258en
dc.rights.embargoperiodNot knownen
rioxxterms.versionofrecord10.1002/sim.6381en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2015-03-15en
rioxxterms.typeJournal Article/Reviewen
plymouth.oa-locationhttp://onlinelibrary.wiley.com/doi/10.1002/sim.6381/abstracten


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