Hierarchical Spatio-Temporal Mapping of Disease Rates
本文扩展了现有的分层空间模型，加入时间效应和时空交互作用，并用俄亥俄州1968-1988年县级肺癌数据演示了如何通过MCMC方法拟合高参数模型。
Abstract Maps of regional morbidity and mortality rates are useful tools in determining spatial patterns of disease. Combined with sociodemographic census information, they also permit assessment of environmental justice; that is, whether certain subgroups suffer disproportionately from certain diseases or other adverse effects of harmful environmental exposures. Bayes and empirical Bayes methods have proven useful in smoothing crude maps of disease risk, eliminating the instability of estimates in low-population areas while maintaining geographic resolution. In this article we extend existing hierarchical spatial models to account for temporal effects and spatio-temporal interactions. Fitting the resulting highly parameterized models requires careful implementation of Markov chain Monte Carlo (MCMC) methods, as well as novel techniques for model evaluation and selection. We illustrate our approach using a dataset of county-specific lung cancer rates in the state of Ohio during the period 1968–1988.