Bayesian Age-Period-Cohort-Modelling and Prediction.
Background: Coronary Heart Disease (CHD) remains a major cause of mortality in the United Kingdom. Yet predictions of future CHD mortality are potentially problematic due to population ageing and increase in obesity and diabetes. Here we explore …
This thesis is concerned with the analysis of spatial and temporal structures of epidemiological data using modern Bayes techniques. Mainly autoregressive distributions as Gaussian Markov random fields or random walks are used as smoothing priors.
Such extensive models can be estimated using MCMC methods only. Some effective algorithms are introduced to get estimates in acceptable time. Especially for space time interactions such algorithms are essential.
As example spatial Bayesian models are applied for wildlife disease incidence data.
We apply a full Bayesian model framework to a dataset on stomach cancer mortality in West Germany. The data are stratified by age group, year, and district. Using an age-period-cohort model with an additional spatial component, our goal is to …