Disease Mapping of Stage-specific Cancer Incidence Data

We propose two approaches for the spatial analysis of cancer
incidence data with additional information on the stage of the
disease at time of diagnosis. The two formulations are extensions
of commonly used models for multicategorical response data on an
ordinal scale. We include spatial and age group effects in both
formulations, which we estimate in a nonparametric smooth way. More
specifically, we adopt a fully Bayesian approach based on Gaussian
pairwise difference priors where additional smoothing parameters
are treated as unknown as well. We apply our methods to data on
cervical cancer in the former German Democratic Republic. The
results suggest that there are large spatial differences in the
stage-proportions, which indicates spatial variability with respect
to the introduction and effectiveness of screening programs.