The spatio-temporal modeling of prostate cancer in Limburg

Abstract

The main aim of this study was to assess the evolution of prostate cancer disease risk in Limburg, found in the north-east of Belgium. The data analyzed include yearly incidence counts from prostate cancer, which was subdivided according to 18 age groups in the male population as observed in each of these municipalities during the 1996-2005 period in Limburg. To address these main objectives, the data were analysed using several Bayesian hierarchical models, which accounts for the spatial and temporal effects as random effects, through prior distributions. In general, we examined four models for the spatial only data, and thirteen inseparable space-time interactions with two separable models, for the spatio-temporal dataset. The results of the Bayesian hierarchical models have typically been presented in the form of maps displaying the mean of the posterior distribution of the relative risk for each municipality. In this particular study, the results suggested that the time trends for every municipality do not rely on a parametric shape, but flexible to describe the variety of time trends that arise in the data. In conclusion, we have seen that sharing information among municipalities has been shown to improve the model more than sharing information among periods. This suggested in general that the spatial dependence is very important to describe the behavior of the risk in this specific data, indeed higher than the temporal one.