Modelling categorical covariates in Bayesian disease mapping by partition structures

Giudici, P.; Knorr-Held, Leonhard and Rasser, G. (1999): Modelling categorical covariates in Bayesian disease mapping by partition structures. Collaborative Research Center 386, Discussion Paper 152

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.

• Item Type: Paper
• Faculties: Mathematics, Computer Science and Statistics > Statistics > Collaborative Research Center 386; Special Research Fields > Special Research Field 386
• Subjects: 500 Science > 510 Mathematics
• Language: English