A Bayesian two-stage spatially dependent variable selection model for space–time health data. (September 2019)

• Record Type: Journal Article
• Authors: Choi, Jungsoon; Lawson, Andrew B
• Is Part Of: Statistical methods in medical research. Volume 28:Number 9(2019)
• Page Start: 2570
• Page End: 2582
• Publication Date: 2019-09
• Subjects: Spatial confounding problem -- Bayesian spatial variable selection -- spatial random component; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280218767980
• Languages: English
• ISSNs: 0962-2802

Abstract

In space–time epidemiological modeling, most studies have considered the overall variations in relative risk to better estimate the effects of risk factors on health outcomes. However, the associations between risk factors and health outcomes may vary across space and time. Especially, the temporal patterns of the covariate effects may depend on space. Thus, we propose a Bayesian two-stage spatially dependent variable selection approach for space–time health data to determine the spatially varying subsets of regression coefficients with common temporal dependence. The two-stage structure allows reduction of the spatial confounding bias in the estimates of the regression coefficients. A simulation study is conducted to examine the performance of the proposed two-stage model. We apply the proposed model to the number of inpatients with lung cancer in 159 counties of Georgia, USA.