Bayesian modelling of space-time interactions on the Lexis diagram

We propose a full model-based framework for a statistical analysis
of incidence or mortality count data stratified by age, period and
space, with specific inclusion of additional cohort effects. The
setup will be fully Bayesian based on a series of Gaussian Markov
random field priors for each of the components. Additional
space-time interactions will be either modelled as space-period or
space-cohort effects. Statistical inference is based on efficient
algorithms to block update Gaussian Markov random fields, which
have recently been proposed in the literature. We illustrate our
approach in an analysis of stomach cancer data in West Germany.