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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/14869

Title: A generalized Poisson-gamma model for spatially overdispersed data.
Authors: Neyens, Thomas
Faes, Christel
Molenberghs, Geert
Issue Date: 2012
Citation: Spatial and Spatio-temporal Epidemiology, 3 (3), p. 185-194
Abstract: Modern disease mapping commonly uses hierarchical Bayesian methods to model overdispersion and spatial correlation. Classical random-e ects based solutions include the Poisson-gamma model, which uses the conjugacy between the Poisson and gamma distributions, but which does not model spatial correlation, on the one hand, and the more advanced CAR model, which also introduces a spatial autocorrelation term but without a closed-form posterior distribution on the other. In this paper, a combined model is proposed: an alternative convolution model accounting for both overdispersion and spatial correlation in the data by combining the Poisson-gamma model with a spatially-structured normal CAR random e ect. The Limburg Cancer Registry data on kidney and prostate cancer in Limburg were used to compare the conventional and new models. A simulation study con rmed results and interpretations coming from the real datasets. Relative risk maps showed that the combined model provides an intermediate between the non-patterned negative binomial and the sometimes oversmoothed CAR convolution model.
URI: http://hdl.handle.net/1942/14869
DOI: 10.1016/j.sste.2011.10.004
ISSN: 1877-5845
Category: A1
Type: Journal Contribution
Validation: vabb, 2014
Appears in Collections: Research publications

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