Modelling categorical covariates in Bayesian disease mapping by partition structures (1999)
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BibTeX
@TECHREPORT{Giudici99modellingcategorical,
author = {Paolo Giudici and Leonhard Knorr-Held and Günter Raßer},
title = {Modelling categorical covariates in Bayesian disease mapping by partition structures},
institution = {},
year = {1999}
}
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Abstract
We consider the problem of mapping the risk from a disease using a series of regional counts of observed and expected cases, and information on potential risk factors. To analyse this problem from a Bayesian viewpoint we propose a methodology, which extends a spatial partition model by including categorical covariate information. Such an extension allows to detect clusters in the residual variation, reflecting further, possibly unobserved, covariates. The methodology is implemented by means of reversible jump Markov chain Monte Carlo sampling. An application is presented, in order to illustrate and compare our proposed extensions with a purely spatial partition model. Here we analyse a well-known dataset on lip cancer incidence in Scotland. 1 Introduction Statistical methods for analysing the geographical variation of disease rates have received increasing interest in the recent literature. Bayesian approaches to this problem typically introduce parameters, which may or may n...
Keyphrases
bayesian disease mapping partition structure categorical covariates spatial partition model categorical covariate information recent literature lip cancer incidence introduction statistical method bayesian viewpoint regional count residual variation potential risk factor disease rate bayesian approach expected case introduce parameter geographical variation well-known dataset
