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A Bayesian analysis for spatial processes with application to disease mapping
Author(s) -
Sue Bell B.,
Broemeling Lyle D.
Publication year - 2000
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/(sici)1097-0258(20000415)19:7<957::aid-sim396>3.0.co;2-q
Subject(s) - bayesian probability , computer science , statistics , data mining , econometrics , artificial intelligence , mathematics
In epidemiology, maps of disease rates and disease risk provide a spatial perspective for researching disease aetiology. For rare diseases or when the population base is small, the rate and risk estimates may be unstable. We propose using a Bayesian analysis based on the conditional autoregressive (CAR) process that will spatially smooth disease rates or risk estimates by allowing each site to ‘borrow strength’ from its neighbours. Covariates may be included in the model in such a way as to establish a possible association between risk factors and disease incidence. Bayesian inferences are implemented from a direct resampling scheme where large samples are generated from the various posterior distributions. The methodology is demonstrated with a simulation that assesses the effect of sample size and the model parameters on inferences for the parameters. Our approach is also used to spatially smooth district lip cancer rates in Scotland using the CAR model with a covariate that allows for exposure to sunlight. Copyright © 2000 John Wiley & Sons, Ltd.