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dc.contributor.authorKang, Su Yun
dc.contributor.authorMcGree, James
dc.contributor.authorBaade, Peter
dc.contributor.authorMengersen, Kerrie
dc.date.accessioned2017-06-09T02:22:53Z
dc.date.available2017-06-09T02:22:53Z
dc.date.issued2015
dc.identifier.issn1369-1473
dc.identifier.doi10.1111/anzs.12127
dc.identifier.urihttp://hdl.handle.net/10072/141188
dc.description.abstractResearchers familiar with spatial models are aware of the challenge of choosing the level of spatial aggregation. Few studies have been published on the investigation of temporal aggregation and its impact on inferences regarding disease outcome in space–time analyses. We perform a case study for modelling individual disease outcomes using several Bayesian hierarchical spatio-temporal models, while taking into account the possible impact of spatial and temporal aggregation. Using longitudinal breast cancer data from South East Queensland, Australia, we consider both parametric and non-parametric formulations for temporal effects at various levels of aggregation. Two temporal smoothness priors are considered separately; each is modelled with fixed effects for the covariates and an intrinsic conditional autoregressive prior for the spatial random effects. Our case study reveals that different model formulations produce considerably different model performances. For this particular dataset, a classical parametric formulation that assumes a linear time trend produces the best fit among the five models considered. Different aggregation levels of temporal random effects were found to have little impact on model goodness-of-fit and estimation of fixed effects.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherWiley-Blackwell Publishing Asia
dc.relation.ispartofpagefrom325
dc.relation.ispartofpageto345
dc.relation.ispartofissue3
dc.relation.ispartofjournalAustralian and New Zealand Journal of Statistics
dc.relation.ispartofvolume57
dc.subject.fieldofresearchStatistics not elsewhere classified
dc.subject.fieldofresearchStatistics
dc.subject.fieldofresearchEconometrics
dc.subject.fieldofresearchcode010499
dc.subject.fieldofresearchcode0104
dc.subject.fieldofresearchcode1403
dc.titleA Case Study for Modelling Cancer Incidence Using Bayesian Spatio-Temporal Models
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dc.description.versionPost-print
gro.rights.copyright© 2015 Australian Statistical Publishing Association. This is the peer reviewed version of the following article: A Case Study for Modelling Cancer Incidence Using Bayesian Spatio-Temporal Models, Australian & New Zealand Journal of Statistics, Vol. 57(3), pp. 325–345, 2015 which has been published in final form at http://dx.doi.org/10.1111/anzs.12127. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving (http://olabout.wiley.com/WileyCDA/Section/id-828039.html)
gro.hasfulltextFull Text
gro.griffith.authorBaade, Peter D.


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