Disease mapping and regression with count data in the presence of overdispersion and spatial autocorrelation: a Bayesian model averaging approach Mohebbi, Mohammadreza, Wolfe, Rory and Forbes, Andrew 2014, Disease mapping and regression with count data in the presence of overdispersion and spatial autocorrelation: a Bayesian model averaging approach, International journal of environmental research and public health, vol. 11, no. 1, pp. 883-902, doi: 10.3390/ijerph110100883. Attached Files Name Description MIMEType Size Downloads mohebbi-diseasemapping-2014.pdf Published version application/pdf 1.16MB 63 Title Disease mapping and regression with count data in the presence of overdispersion and spatial autocorrelation: a Bayesian model averaging approach Author(s) Mohebbi, Mohammadreza Wolfe, Rory Forbes, Andrew Journal name International journal of environmental research and public health Volume number 11 Issue number 1 Start page 883 End page 902 Total pages 10 Publisher Multidisciplinary Digital Publishing Institute (MDPI) Place of publication Basel, Switzerland Publication date 2014 ISSN 1661-7827 Keyword(s) Bayesian variable selection cancer disease mapping ecologic studies Gibbs sampling spatial epidemiology Summary This paper applies the generalised linear model for modelling geographical variation to esophageal cancer incidence data in the Caspian region of Iran. The data have a complex and hierarchical structure that makes them suitable for hierarchical analysis using Bayesian techniques, but with care required to deal with problems arising from counts of events observed in small geographical areas when overdispersion and residual spatial autocorrelation are present. These considerations lead to nine regression models derived from using three probability distributions for count data: Poisson, generalised Poisson and negative binomial, and three different autocorrelation structures. We employ the framework of Bayesian variable selection and a Gibbs sampling based technique to identify significant cancer risk factors. The framework deals with situations where the number of possible models based on different combinations of candidate explanatory variables is large enough such that calculation of posterior probabilities for all models is difficult or infeasible. The evidence from applying the modelling methodology suggests that modelling strategies based on the use of generalised Poisson and negative binomial with spatial autocorrelation work well and provide a robust basis for inference. Language eng DOI 10.3390/ijerph110100883 Field of Research 179999 Psychology and Cognitive Sciences not elsewhere classified Socio Economic Objective 970117 Expanding Knowledge in Psychology and Cognitive Sciences HERDC Research category C1 Refereed article in a scholarly journal Copyright notice ©2014, Multidisciplinary Digital Publishing Institute (MDPI) Persistent URL http://hdl.handle.net/10536/DRO/DU:30066326 Document type: Journal Article Collections: Faculty of Health Open Access Collection Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Thu, 02 Oct 2014, 09:18:57 EST Fri, 03 Oct 2014, 14:59:00 EST Mon, 15 Dec 2014, 10:12:14 EST Mon, 23 Nov 2015, 09:32:57 EST Filtered Full Citation counts:   Cited 2 times in TR Web of Science Cited 3 times in Scopus Search Google Scholar Access Statistics: 330 Abstract Views, 63 File Downloads  -  Detailed Statistics Created: Thu, 02 Oct 2014, 09:18:57 EST

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