Exploratory quantile regression with many covariates: an application to adverse birth outcomes.


Journal Article

Covariates may affect continuous responses differently at various points of the response distribution. For example, some exposure might have minimal impact on conditional means, whereas it might lower conditional 10th percentiles sharply. Such differential effects can be important to detect. In studies of the determinants of birth weight, for instance, it is critical to identify exposures like the one above, since low birth weight is a risk factor for later health problems. Effects of covariates on the tails of distributions can be obscured by models (such as linear regression) that estimate conditional means; however, effects on tails can be detected by quantile regression. We present 2 approaches for exploring high-dimensional predictor spaces to identify important predictors for quantile regression. These are based on the lasso and elastic net penalties. We apply the approaches to a prospective cohort study of adverse birth outcomes that includes a wide array of demographic, medical, psychosocial, and environmental variables. Although tobacco exposure is known to be associated with lower birth weights, the analysis suggests an interesting interaction effect not previously reported: tobacco exposure depresses the 20th and 30th percentiles of birth weight more strongly when mothers have high levels of lead in their blood compared with those who have low blood lead levels.

Full Text

Duke Authors

Cited Authors

  • Burgette, LF; Reiter, JP; Miranda, ML

Published Date

  • November 2011

Published In

Volume / Issue

  • 22 / 6

Start / End Page

  • 859 - 866

PubMed ID

  • 21968775

Pubmed Central ID

  • 21968775

Electronic International Standard Serial Number (EISSN)

  • 1531-5487

International Standard Serial Number (ISSN)

  • 1044-3983

Digital Object Identifier (DOI)

  • 10.1097/ede.0b013e31822908b3


  • eng