title: A comparison of conditional autoregressive models used in Bayesian disease mapping.
abstract: 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.
authors: School of Mathematics and Statistics, University Gardens, University of Glasgow, Glasgow G12 8QW, United Kingdom. Duncan.Lee@glasgow.ac.uk
authors: Duncan
authors: D
authors: Lee
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004196:Disease Outbreaks / Q000706:statistics & numerical data*; D062306:Geography, Medical; D006801:Humans; D015233:Models, Statistical; D009369:Neoplasms / Q000453:epidemiology*; D012306:Risk; D012606:Scotland / Q000453:epidemiology; D062206:Spatial Analysis