Modeling Adverse Birth Outcomes via Confirmatory Factor Quantile Regression

We describe a Bayesian quantile regression model that uses a confirmatory factor structure for part of the design matrix. This model is appropriate when the covariates are indicators of scientifically determined latent factors, and it is these latent factors that analysts seek to include as predicto...

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Bibliographic Details
Published in:Biometrics 2012-03, Vol.68 (1), p.92-100
Main Authors: Burgette, Lane F., Reiter, Jerome P.
Format: Article
Language:English
Subjects:
Bayes Theorem
Bayesian analysis
Bayesian inference
Bayesian networks
BIOMETRIC METHODOLOGY
Biometrics
biometry
Birth Weight
Causality
Female
Fetal Growth Retardation - epidemiology
Gibbs sampling
Humans
Infant, Low Birth Weight
Infant, Newborn
Infant, Very Low Birth Weight
Low birth weight
Maternal Exposure - statistics & numerical data
Mathematical vectors
Modeling
Pregnancy
pregnancy complications
Prevalence
Proportional Hazards Models
Quantile regression
Random variables
Regression Analysis
tobacco
Tobacco Smoke Pollution - statistics & numerical data
Tobacco smoking
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Summary:We describe a Bayesian quantile regression model that uses a confirmatory factor structure for part of the design matrix. This model is appropriate when the covariates are indicators of scientifically determined latent factors, and it is these latent factors that analysts seek to include as predictors in the quantile regression. We apply the model to a study of birth weights in which the effects of latent variables representing psychosocial health and actual tobacco usage on the lower quantiles of the response distribution are of interest. The models can be fit using an R package called factorQR.
ISSN:0006-341X
1541-0420
DOI:10.1111/j.1541-0420.2011.01639.x