Bayesian spatially dependent variable selection for small area health modeling.

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 Res

authors

Choi J,Lawson AB

doi

10.1177/0962280215627184

subject

Has Abstract

pub_date

2018-01-01 00:00:00

pages

234-249

issue

1

eissn

0962-2802

issn

1477-0334

pii

0962280215627184

journal_volume

27

pub_type

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