Abstract:
: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.
journal_name
Stat Methods Med Resjournal_title
Statistical methods in medical researchauthors
Choi J,Lawson ABdoi
10.1177/0962280215627184subject
Has Abstractpub_date
2018-01-01 00:00:00pages
234-249issue
1eissn
0962-2802issn
1477-0334pii
0962280215627184journal_volume
27pub_type
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