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dc.contributor.authorJackson, Christopheren
dc.contributor.authorPresanis, Anneen
dc.contributor.authorConti, Stefanoen
dc.contributor.authorDe Angelis, Danielaen
dc.date.accessioned2019-02-08T00:31:07Z
dc.date.available2019-02-08T00:31:07Z
dc.date.issued2019-01en
dc.identifier.issn0162-1459
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/288919
dc.description.abstractSuppose we have a Bayesian model which combines evidence from several different sources. We want to know which model parameters most affect the estimate or decision from the model, or which of the parameter uncertainties drive the decision uncertainty. Furthermore we want to prioritise what further data should be collected. These questions can be addressed by Value of Information (VoI) analysis, in which we estimate expected reductions in loss from learning specific parameters or collecting data of a given design. We describe the theory and practice of VoI for Bayesian evidence synthesis, using and extending ideas from health economics, computer modelling and Bayesian design. The methods are general to a range of decision problems including point estimation and choices between discrete actions. We apply them to a model for estimating prevalence of HIV infection, combining indirect information from several surveys, registers and expert beliefs. This analysis shows which parameters contribute most of the uncertainty about each prevalence estimate, and provides the expected improvements in precision from collecting specific amounts of additional data.
dc.format.mediumElectronic-eCollectionen
dc.languageengen
dc.publisherTaylor & Francis
dc.titleValue of Information: Sensitivity Analysis and Research Design in Bayesian Evidence Synthesis.en
dc.typeArticle
prism.endingPage1449
prism.issueIdentifier528en
prism.publicationDate2019en
prism.publicationNameJournal of the American Statistical Associationen
prism.startingPage1436
prism.volume114en
dc.identifier.doi10.17863/CAM.36182
dcterms.dateAccepted2018-12-06en
rioxxterms.versionofrecord10.1080/01621459.2018.1562932en
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2019-01en
dc.contributor.orcidJackson, Christopher [0000-0002-6656-8913]
dc.contributor.orcidPresanis, Anne [0000-0003-3078-4427]
dc.contributor.orcidDe Angelis, Daniela [0000-0001-6619-6112]
dc.identifier.eissn1537-274X
rioxxterms.typeJournal Article/Reviewen
cam.orpheus.successThu Jan 30 10:52:34 GMT 2020 - Embargo updated*
rioxxterms.freetoread.startdate2020-10-02


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