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Spatiotemporal Bayesian hierarchical...

The Florida State University.

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Spatiotemporal Bayesian hierarchical models, with application to birth outcomes.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Spatiotemporal Bayesian hierarchical models, with application to birth outcomes./
Author:	Norton, Jonathan D.
Description:	89 p.
Notes:	Source: Dissertation Abstracts International, Volume: 69-07, Section: B, page: 3929.
Contained By:	Dissertation Abstracts International69-07B.
Subject:	Biology, Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3321516
ISBN:	9780549730859

Spatiotemporal Bayesian hierarchical models, with application to birth outcomes.
Norton, Jonathan D.

Spatiotemporal Bayesian hierarchical models, with application to birth outcomes. - 89 p.

Source: Dissertation Abstracts International, Volume: 69-07, Section: B, page: 3929.

Thesis (Ph.D.)--The Florida State University, 2008.

A class of hierarchical Bayesian models is introduced for adverse birth outcomes such as preterm birth, which are assumed to follow a conditional binomial distribution. The log-odds of an adverse outcome in a particular county, logit(pi), follows a linear model which includes observed covariates and normally-distributed random effects. Spatial dependence between neighboring regions is allowed for by including an intrinsic autoregressive (IAR) prior or an 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 same results are also shown to hold when the IAR is replaced by a conditional autoregressive (CAR) model. Furthermore, properties of the CAR parameter rho are explored. The Deviance Information Criterion (DIC) is considered as a way to compare spatial hierarchical models. Simulations are performed to test whether the DIC can identify whether binomial outcomes come from an IAR, an IAR convolution, or independent normal deviates. Having established the theoretical foundations of the class of models and 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 to 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 strikingly simple model that includes a constant spatial effect for all periods, a linear trend, and three covariates. It is also found that the risks of each outcome are increasing over time, even with adjustment for covariates.

ISBN: 9780549730859Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Spatiotemporal Bayesian hierarchical models, with application to birth outcomes.
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245 1 0 $a Spatiotemporal Bayesian hierarchical models, with application to birth outcomes.
300 $a 89 p.
500 $a Source: Dissertation Abstracts International, Volume: 69-07, Section: B, page: 3929.
502 $a Thesis (Ph.D.)--The Florida State University, 2008.
520 $a A class of hierarchical Bayesian models is introduced for adverse birth outcomes such as preterm birth, which are assumed to follow a conditional binomial distribution. The log-odds of an adverse outcome in a particular county, logit(pi), follows a linear model which includes observed covariates and normally-distributed random effects. Spatial dependence between neighboring regions is allowed for by including an intrinsic autoregressive (IAR) prior or an 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 same results are also shown to hold when the IAR is replaced by a conditional autoregressive (CAR) model. Furthermore, properties of the CAR parameter rho are explored. The Deviance Information Criterion (DIC) is considered as a way to compare spatial hierarchical models. Simulations are performed to test whether the DIC can identify whether binomial outcomes come from an IAR, an IAR convolution, or independent normal deviates. Having established the theoretical foundations of the class of models and 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 to 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 strikingly simple model that includes a constant spatial effect for all periods, a linear trend, and three covariates. It is also found that the risks of each outcome are increasing over time, even with adjustment for covariates.
590 $a School code: 0071.
650 4 $a Biology, Biostatistics. $3 1018416
650 4 $a Health Sciences, Obstetrics and Gynecology. $3 1020690
650 4 $a Health Sciences, Public Health. $3 1017659
690 $a 0308
690 $a 0380
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710 2 $a The Florida State University. $3 1017727
773 0 $t Dissertation Abstracts International $g 69-07B.
790 $a 0071
791 $a Ph.D.
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