title: Modeling adverse birth outcomes via confirmatory factor quantile regression.
abstract: 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.
authors.affiliation: Department of Statistical Science, Duke University, Durham, North Carolina 27708, USA. lb131@stat.duke.edu
authors.forename: Lane F
authors.initials: LF
authors.lastname: Burgette
authors.forename: Jerome P
authors.initials: JP
authors.lastname: Reiter
mesh_terms: D001499:Bayes Theorem; D001724:Birth Weight; D015984:Causality; D005260:Female; D005317:Fetal Growth Retardation / Q000453:epidemiology*; D006801:Humans; D007230:Infant, Low Birth Weight; D007231:Infant, Newborn; D019102:Infant, Very Low Birth Weight; D018811:Maternal Exposure / Q000706:statistics & numerical data*; D015995:Prevalence; D016016:Proportional Hazards Models; D012044:Regression Analysis; D014028:Tobacco Smoke Pollution / Q000706:statistics & numerical data*
chemical_list: D014028:Tobacco Smoke Pollution