Intrinsically Autoregressive Spatiotemporal Models With Application to Aggregated Birth Outcomes
成果类型：
Article
署名作者：
Norton, Jonathan D.; Niu, Xu-Feng
署名单位：
US Food & Drug Administration (FDA); State University System of Florida; Florida State University
刊物名称：
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
ISSN/ISSBN：
0162-1459
DOI：
10.1198/jasa.2009.0030
发表日期：
2009
页码：
638-649
关键词：
preterm birth
disease
SPACE
restoration
CONVERGENCE
statistics
pregnancy
fertility
rates
time
摘要：
A class of hierarchical Bayesian models is proposed for adverse birth outcomes such as preterm birth, which are conditional binomial distribution. The log-odds of an adverse outcome in a particular county, logit(p(i)), follow a linear model that includes observed covariates and normally-distributed random effects. Spatial dependence between neighboring regions is allowed for by including an intrinsically autoregressive (IAR) prior or air IAR convolution prior in the linear predictor. Temporal dependence is incorporated by including a temporal IAR term also. It is shown that the variance parameters underlying these random effects (IAR, convolution, convolution plus temporal IAR) are identifiable. The Deviance Information Criterion (DIC) is considered as a way to compare spatial hierarchical models. Simulations are performed to test whether the DIC call identify whether binomial outcomes come front a hierarchical model that includes different combinations of random and fixed effects. Having validated the DIC as a means of comparing models, we examine preterm birth and low birth weight counts in the state of Arkansas from 1994-2005. We find that preterm birth and low birth weight have different spatial patterns of risk, and that rates of low birth weight can be fit with a relatively simple model that includes a constant spatial effect for all periods. a linear trend. and three covariates (multiple birth. black mother, smoking). It is also found that the risks of each outcome are increasing over time, even with adjustment for covariate.