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Title: Spatiotemporal multivariate mixture models for Bayesian model selection in disease mapping
Authors: LAWSON, Andrew 
Carroll, Rachel
FAES, Christel 
Kirby, Russell S.
AREGAY, Mehreteab 
WATJOU, Kevin 
Issue Date: 2017
Publisher: WILEY
Source: ENVIRONMETRICS, 28(8) (Art N° e2465)
Abstract: It is often the case that researchers wish to simultaneously explore the behavior of, and estimate the 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 spatiotemporal 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, socioeconomic, 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 the simulation ground truth and display an improved model fit over two baseline Knorr-Held spatiotemporal interaction model variants in a real data application.
Notes: [Lawson, Andrew B.; Carroll, Rachel; Aregay, Mehreteab] Med Univ South Carolina, Dept Publ Hlth Sci, 135 Cannon St, Charleston, SC 29425 USA. [Faes, Christel; Watjou, Kevin] Hasselt Univ, Interuniv Inst Stat & Stat Bioinformat, Hasselt, Belgium. [Kirby, Russell S.] Univ S Florida, Dept Community & Family Hlth, Tampa, FL 33612 USA.
Keywords: MCMC; mixture model; model selection; Poisson; shared components;MCMC; mixture model; model selection; Poisson; shared components
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ISSN: 1180-4009
e-ISSN: 1099-095X
DOI: 10.1002/env.2465
ISI #: 000417157600002
Category: A1
Type: Journal Contribution
Validations: ecoom 2018
Appears in Collections:Research publications

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