Document Detail


Joint Bayesian analysis of birthweight and censored gestational age using finite mixture models.
MedLine Citation:
PMID:  20575047     Owner:  NLM     Status:  In-Process    
Abstract/OtherAbstract:
Birthweight and gestational age are closely related and represent important indicators of a healthy pregnancy. Customary modeling for birthweight is conditional on gestational age. However, joint modeling directly addresses the relationship between gestational age and birthweight, and provides increased flexibility and interpretation as well as a strategy to avoid using gestational age as an intermediate variable. Previous proposals have utilized finite mixtures of bivariate regression models to incorporate well-established risk factors into analysis (e.g. sex and birth order of the baby, maternal age, race, and tobacco use) while examining the non-Gaussian shape of the joint birthweight and gestational age distribution. We build on this approach by demonstrating the inferential (prognostic) benefits of joint modeling (e.g. investigation of 'age inappropriate' outcomes like small for gestational age) and hence re-emphasize the importance of capturing the non-Gaussian distributional shapes. We additionally extend current models through a latent specification which admits interval-censored gestational age. We work within a Bayesian framework which enables inference beyond customary parameter estimation and prediction as well as exact uncertainty assessment. The model is applied to a portion of the 2003-2006 North Carolina Detailed Birth Record data (n=336129) available through the Children's Environmental Health Initiative and is fitted using the Bayesian methodology and Markov chain Monte Carlo approaches.
Authors:
Scott L Schwartz; Alan E Gelfand; Marie L Miranda
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Statistics in medicine     Volume:  29     ISSN:  1097-0258     ISO Abbreviation:  Stat Med     Publication Date:  2010 Jul 
Date Detail:
Created Date:  2010-06-29     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8215016     Medline TA:  Stat Med     Country:  England    
Other Details:
Languages:  eng     Pagination:  1710-23     Citation Subset:  IM    
Copyright Information:
Copyright (c) 2010 John Wiley & Sons, Ltd.
Affiliation:
Department of Statistical Science, Duke University, Durham, NC 27708-0251, USA. scott.schwartz@stat.duke.edu
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