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dc.contributor.authorJackson, Danen
dc.contributor.authorLaw, Martinen
dc.contributor.authorBarrett, Jessicaen
dc.contributor.authorTurner, Rebeccaen
dc.contributor.authorHiggins, Julian PTen
dc.contributor.authorSalanti, Georgiaen
dc.contributor.authorWhite, Ian Ren
dc.date.accessioned2015-10-06T12:33:33Z
dc.date.available2015-10-06T12:33:33Z
dc.date.issued2015-09-30en
dc.identifier.citationJackson et al. Statistics in Medicine (2015) Vol. 35, pp. 819-839. doi: 10.1002/sim.6752en
dc.identifier.issn0277-6715
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/251312
dc.description.abstractNetwork meta-analysis is becoming more popular as a way to compare multiple treatments simultaneously. Here, we develop a new estimation method for fitting models for network meta-analysis with random inconsistency effects. This method is an extension of the procedure originally proposed by DerSimonian and Laird. Our methodology allows for inconsistency within the network. The proposed procedure is semi-parametric, non-iterative, fast and highly accessible to applied researchers. The methodology is found to perform satisfactorily in a simulation study provided that the sample size is large enough and the extent of the inconsistency is not very severe. We apply our approach to two real examples.
dc.description.sponsorshipDJ, RT and IRW are employed by the UK Medical Research Council (code U105260558). JB is supported by the UK MRC grant numbers G0902100 and MR/K014811/1.
dc.languageEnglishen
dc.language.isoenen
dc.publisherWiley
dc.rightsAttribution 2.0 UK: England & Wales*
dc.rights.urihttp://creativecommons.org/licenses/by/2.0/uk/*
dc.subjectmethod of momentsen
dc.subjectmixed treatment comparisonsen
dc.subjectmultiple treatments meta-analysisen
dc.subjectnetwork meta-analysisen
dc.titleExtending DerSimonian and Laird's methodology to perform network meta-analyses with random inconsistency effectsen
dc.typeArticle
dc.description.versionThis is the final version of the article. It first appeared from Wiley via http://dx.doi.org/10.1002/sim.6752en
prism.endingPage839
prism.publicationDate2015en
prism.publicationNameStatistics in Medicineen
prism.startingPage819
prism.volume35en
dc.rioxxterms.funderMRC
dc.rioxxterms.projectidU105260558
dc.rioxxterms.projectidG0902100
dc.rioxxterms.projectidMR/K014811/1
dcterms.dateAccepted2015-09-13en
rioxxterms.versionofrecord10.1002/sim.6752en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2015-09-30en
dc.contributor.orcidLaw, Martin [0000-0001-9594-348X]
dc.contributor.orcidBarrett, Jessica [0000-0003-1889-9803]
dc.identifier.eissn1097-0258
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idMRC (MR/L501566/1)
pubs.funder-project-idMRC (MR/K014811/1)
pubs.funder-project-idMRC (MR/L003120/1)
pubs.funder-project-idNational Institute for Health Research (NIHR) (via Royal Brompton & Harefield NHS Foundation Trust) (unknown)
pubs.funder-project-idBritish Heart Foundation (RG/08/014/24067)


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Attribution 2.0 UK: England & Wales
Except where otherwise noted, this item's licence is described as Attribution 2.0 UK: England & Wales