A comparison of Bayesian spatial models for disease mapping. Export

@article{citeulike:674809, abstract = {With the advent of routine health data indexed at a fine geographical resolution, small area disease mapping studies have become an established technique in geographical epidemiology. The specific issues posed by the sparseness of the data and possibility for local spatial dependence belong to a generic class of statistical problems involving an underlying (latent) spatial process of interest corrupted by observational noise. These are naturally formulated within the framework of hierarchical models, and over the past decade, a variety of spatial models have been proposed for the latent level(s) of the hierarchy. In this article, we provide a comprehensive review of the main classes of such models that have been used for disease mapping within a Bayesian estimation paradigm, and report a performance comparison between representative models in these classes, using a set of simulated data to help illustrate their respective properties. We also consider recent extensions to model the joint spatial distribution of multiple disease or health indicators. The aim is to help the reader choose an appropriate structural prior for the second level of the hierarchical model and to discuss issues of sensitivity to this choice.}, address = {Small Area Health Statistics Unit, Department of Epidemiology and Public Health, Imperial College Faculty of Medicine, Norfolk Place, London W2 1PG, UK. n.best@ic.ac.uk}, author = {Best, N. and Richardson, S. and Thomson, A.}, citeulike-article-id = {674809}, citeulike-linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/15690999}, citeulike-linkout-1 = {http://www.hubmed.org/display.cgi?uids=15690999}, issn = {0962-2802}, journal = {Stat Methods Med Res}, keywords = {bayesian, clustering, disease, epidemiology, health, mapping, spatial}, month = {February}, number = {1}, pages = {35--59}, posted-at = {2008-10-21 16:27:06}, priority = {2}, title = {A comparison of Bayesian spatial models for disease mapping.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/15690999}, volume = {14}, year = {2005} }

TY - JOUR ID - citeulike:674809 L3 - citeulike-article-id:674809 N2 - With the advent of routine health data indexed at a fine geographical resolution, small area disease mapping studies have become an established technique in geographical epidemiology. The specific issues posed by the sparseness of the data and possibility for local spatial dependence belong to a generic class of statistical problems involving an underlying (latent) spatial process of interest corrupted by observational noise. These are naturally formulated within the framework of hierarchical models, and over the past decade, a variety of spatial models have been proposed for the latent level(s) of the hierarchy. In this article, we provide a comprehensive review of the main classes of such models that have been used for disease mapping within a Bayesian estimation paradigm, and report a performance comparison between representative models in these classes, using a set of simulated data to help illustrate their respective properties. We also consider recent extensions to model the joint spatial distribution of multiple disease or health indicators. The aim is to help the reader choose an appropriate structural prior for the second level of the hierarchical model and to discuss issues of sensitivity to this choice. IS - 1 JF - Stat Methods Med Res SN - 0962-2802 AD - Small Area Health Statistics Unit, Department of Epidemiology and Public Health, Imperial College Faculty of Medicine, Norfolk Place, London W2 1PG, UK. n.best@ic.ac.uk EP - 59 TI - A comparison of Bayesian spatial models for disease mapping. VL - 14 SP - 35 KW - bayesian KW - clustering KW - disease KW - epidemiology KW - health KW - mapping KW - spatial AU - Best, N AU - Richardson, S AU - Thomson, A PY - 2005/02// UR - http://view.ncbi.nlm.nih.gov/pubmed/15690999 ER -