Spatiotemporal multivariate mixture models for Bayesian model selection in disease mapping.

ABSTRACT: 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 m

SUBMITTER: Lawson AB 

PROVIDER: S-EPMC5722237 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature