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dc.contributor.authorCongdon, Pen_US
dc.date.accessioned2018-10-24T16:01:09Z
dc.date.available2018-08-24en_US
dc.date.issued2019-01-01en_US
dc.date.submitted2018-10-16T11:21:19.956Z
dc.identifier.issn0343-2521en_US
dc.identifier.urihttp://qmro.qmul.ac.uk/xmlui/handle/123456789/48663
dc.description.abstract© 2018, The Author(s). Disease mapping applications generally assume homogeneous regression effects and use random intercepts to account for residual spatial dependence. However, there may be local variation in the association between disease and area risk factors. We consider implications for model fit, estimated regression coefficients, and substantive inferences of allowing spatial variability in impacts of area risk factors. An application to suicide in 6791 English small areas shows that average regression coefficients and substantive inferences (e.g. about relative risk) may be considerably affected by allowing spatially varying predictor effects, while fit is improved.en_US
dc.language.isoenen_US
dc.relation.ispartofGeoJournalen_US
dc.rightsCC-BY
dc.titleSpatial heterogeneity in Bayesian disease mappingen_US
dc.typeArticle
dc.rights.holder© 2018, The Author(s)
dc.identifier.doi10.1007/s10708-018-9920-1en_US
pubs.notesNot knownen_US
pubs.publication-statusAccepteden_US
dcterms.dateAccepted2018-08-24en_US


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