Bayesian spatially dependent variable selection for small area health modeling.
Statistical methods for spatial health data to identify the significant covariates associated with the health outcomes are of critical importance. Most studies have developed variable selection approaches in which the covariates included appear within the spatial domain and their effects are fixed across space. However, the impact of covariates on health outcomes may change across space and ignoring this behavior in spatial epidemiology may cause the wrong interpretation of the relations. Thus, the development of a statistical framework for spatial variable selection is important to allow for the estimation of the space-varying patterns of covariate effects as well as the early detection of disease over space. In this paper, we develop flexible spatial variable selection approaches to find the spatially-varying subsets of covariates with significant effects. A Bayesian hierarchical latent model framework is applied to account for spatially-varying covariate effects. We present a simulation example to examine the performance of the proposed models with the competing models. We apply our models to a county-level low birth weight incidence dataset in Georgia.
1 Department of Mathematics, College of Natural Sciences, Hanyang University, Seoul, South Korea.|2 Research Institute for Natural Sciences, Hanyang University, Seoul, South Korea.
Jungsoon
J
Choi
3 Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, Medical University of South Carolina, SC, USA.
Andrew B
AB
Lawson
D001499:Bayes Theorem; D016021:Epidemiologic Studies; D005845:Georgia; D017063:Outcome Assessment, Health Care / Q000706:statistics & numerical data; D062206:Spatial Analysis
Bayesian spatial variable selection; latent model; spatial health data