A comparison of conditional autoregressive models used in Bayesian disease mapping

Lee, D. (2011) A comparison of conditional autoregressive models used in Bayesian disease mapping. Spatial and Spatio-Temporal Epidemiology, 2(2), pp. 79-89. (doi:10.1016/j.sste.2011.03.001)

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Publisher's URL: http://dx.doi.org/10.1016/j.sste.2011.03.001


Disease mapping is the area of epidemiology that estimates the spatial pattern in disease risk over an extended geographical region, so that areas with elevated risk levels can be identified. Bayesian hierarchical models are typically used in this context, which represent the risk surface using a combination of available covariate data and a set of spatial random effects. These random effects are included to model any overdispersion or spatial correlation in the disease data, that has not been accounted for by the available covariate information. The random effects are typically modelled by a conditional autoregressive (CAR) prior distribution, and a number of alternative specifications have been proposed. This paper critiques four of the most common models within the CAR class, and assesses their appropriateness via a simulation study. The four models are then applied to a new study mapping cancer incidence in Greater Glasgow, Scotland, between 2001 and 2005.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Lee, Professor Duncan
Authors: Lee, D.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Spatial and Spatio-Temporal Epidemiology
ISSN (Online):1877-5853

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
538431Allowing for cliffs and slopes in the risk surface when modelling small-area spatial dataDuncan LeeEconomic & Social Research Council (ESRC)ES/I015604/1M&S - STATISTICS