title: Joint Bayesian analysis of birthweight and censored gestational age using finite mixture models.
abstract: 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.affiliation: Department of Statistical Science, Duke University, Durham, NC 27708-0251, USA. scott.schwartz@stat.duke.edu
authors.forename: Scott L
authors.initials: SL
authors.lastname: Schwartz
authors.forename: Alan E
authors.initials: AE
authors.lastname: Gelfand
authors.forename: Marie L
authors.initials: ML
authors.lastname: Miranda
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D001499:Bayes Theorem; D001719:Birth Certificates; D001722:Birth Order; D001724:Birth Weight; D004493:Education / Q000706:statistics & numerical data; D005006:Ethnicity / Q000706:statistics & numerical data; D005260:Female; D005865:Gestational Age; D006801:Humans; D007230:Infant, Low Birth Weight; D007231:Infant, Newborn; D007236:Infant, Small for Gestational Age; D016013:Likelihood Functions; D008297:Male; D017533:Marital Status / Q000706:statistics & numerical data; D008390:Markov Chains; D008423:Maternal Age; D015233:Models, Statistical; D009010:Monte Carlo Method; D009657:North Carolina; D011247:Pregnancy; D047928:Premature Birth / Q000453:epidemiology; D044469:Racial Groups / Q000706:statistics & numerical data; D012044:Regression Analysis; D012907:Smoking / Q000009:adverse effects / Q000453:epidemiology; D016008:Statistical Distributions; D055815:Young Adult