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dc.contributor.authorJackson, Christopher
dc.contributor.authorJohnson, R
dc.contributor.authorDe Nazelle, A
dc.contributor.authorGoel, R
dc.contributor.authorDe Sá, TH
dc.contributor.authorTainio, M
dc.contributor.authorWoodcock, James
dc.date.accessioned2022-02-05T00:30:33Z
dc.date.available2022-02-05T00:30:33Z
dc.date.issued2021-01-01
dc.identifier.issn2194-9263
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/333670
dc.description.abstractHealth impact simulation models are used to predict how a proposed policy or scenario will affect population health outcomes. These models represent the typically-complex systems that describe how the scenarios affect exposures to risk factors for disease or injury (e.g. air pollution or physical inactivity), and how these risk factors are related to measures of population health (e.g. expected survival). These models are informed by multiple sources of data, and are subject to multiple sources of uncertainty. We want to describe which sources of uncertainty contribute most to uncertainty about the estimate or decision arising from the model. Furthermore, we want to decide where further research should be focused to obtain further data to reduce this uncertainty, and what form that research might take. This article presents a tutorial in the use of Value of Information methods for uncertainty analysis and research prioritisation in health impact simulation models. These methods are based on Bayesian decision-theoretic principles, and quantify the expected benefits from further information of different kinds. The expected value of partial perfect information about a parameter measures sensitivity of a decision or estimate to uncertainty about that parameter. The expected value of sample information represents the expected benefit from a specific proposed study to get better information about the parameter. The methods are applicable both to situations where the model is used to make a decision between alternative policies, and situations where the model is simply used to estimate a quantity (such as expected gains in survival under a scenario). This paper explains how to calculate and interpret the expected value of information in the context of a simple model describing the health impacts of air pollution from motorised transport. We provide a general-purpose R package and full code to reproduce the example analyses.
dc.publisherWalter de Gruyter GmbH
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectstat.ME
dc.subjectstat.ME
dc.titleA guide to value of information methods for prioritising research in health impact modelling
dc.typeArticle
dc.publisher.departmentMrc Biostatistics Unit
dc.publisher.departmentDepartment of Public Health And Primary Care
dc.date.updated2022-02-04T10:43:48Z
prism.issueIdentifier1
prism.publicationDate2021
prism.publicationNameEpidemiologic Methods
prism.startingPage20210012
prism.volume10
dc.identifier.doi10.17863/CAM.81089
rioxxterms.versionofrecord10.1515/em-2021-0012
rioxxterms.versionVoR
dc.contributor.orcidJackson, Christopher [0000-0002-6656-8913]
dc.contributor.orcidWoodcock, James [0000-0003-4769-5375]
dc.identifier.eissn2161-962X
rioxxterms.typeJournal Article/Review
cam.issuedOnline2021-11-15
cam.depositDate2022-02-04
pubs.licence-identifierapollo-deposit-licence-2-1
pubs.licence-display-nameApollo Repository Deposit Licence Agreement


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International