It is often the case that researchers wish to simultaneously explore the behavior of and estimate overall risk for multiple, related diseases with varying rarity while accounting for potential spatial and/or temporal correlation. In this paper, we propose a flexible class of multivariate spatio-temporal mixture models to fill this role. Further, these models offer flexibility with the potential for model selection as well as the ability to accommodate lifestyle, socio-economic, and physical environmental variables with spatial, temporal, or both structures. Here, we explore the capability of this approach via a large scale simulation study and examine a motivating data example involving three cancers in South Carolina. The results which are focused on four model variants suggest that all models possess the ability to recover simulation ground truth and display improved model fit over two baseline Knorr-Held spatio-temporal interaction model variants in a real data application.

中文翻译：

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疾病映射中贝叶斯模型选择的时空多元混合模型

通常情况下，研究人员希望同时探索多种相关疾病的行为并估计其总体风险，这些疾病具有不同的罕见性，同时考虑到潜在的空间和/或时间相关性。在本文中，我们提出了一类灵活的多元时空混合模型来填补这一角色。此外，这些模型提供了具有模型选择潜力的灵活性，以及​​适应生活方式、社会经济和物理环境变量与空间、时间或两者结构的能力。在这里，我们通过大规模模拟研究探索了这种方法的能力，并检查了一个涉及南卡罗来纳州三种癌症的激励数据示例。