Generalized linear models with a Poisson distribution are often used to model cancer registry data stratified by sex, age, year, and little geographical units. We compare three different approaches which take into account possible spatial correlation among neighbouring units, using lung cancer incidence data. Inference is fully Bayesian and uses Markov Chain Monte Carlo techniques. Comparison between models is based on the Deviance Information Criterion (DIC).
Comparison of Three Convolution Prior Spatial Models for Cancer Incidence
MUSIO, MONICA;
2007-01-01
Abstract
Generalized linear models with a Poisson distribution are often used to model cancer registry data stratified by sex, age, year, and little geographical units. We compare three different approaches which take into account possible spatial correlation among neighbouring units, using lung cancer incidence data. Inference is fully Bayesian and uses Markov Chain Monte Carlo techniques. Comparison between models is based on the Deviance Information Criterion (DIC).
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