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Exploratory quantile regression with many covariates: an application to adverse birth outcomes
Lane F Burgette1, Jerome P Reiter, Marie Lynn Miranda
1Department of Statistical Science, Duke University, Durham, NC 27708, USA. lb131@stat.duke.edu
Epidemiology (Cambridge, Mass.)
|October 5, 2011
Summary
Quantile regression methods reveal how factors like tobacco and lead exposure impact birth weight percentiles. This analysis identified a novel interaction effect, showing combined exposure significantly lowers birth weight at lower percentiles.
Area of Science:
- Biostatistics
- Epidemiology
- Environmental Health
Background:
- Covariate effects can vary across a response distribution's different points.
- Identifying these differential effects is crucial, especially in birth weight studies where low birth weight is a health risk.
- Traditional models like linear regression, focusing on conditional means, can obscure effects on distribution tails.
Purpose of the Study:
- To present novel approaches for identifying important predictors in high-dimensional spaces for quantile regression.
- To apply these methods to a birth outcomes cohort study.
- To investigate the impact of various demographic, medical, psychosocial, and environmental variables on birth weight distribution.
Main Methods:
- Utilized quantile regression to detect effects on distribution tails.
- Developed and applied two new methods based on lasso and elastic net penalties for high-dimensional predictor selection.
- Analyzed a prospective cohort study of adverse birth outcomes.
Main Results:
- Identified significant predictors for various birth weight quantiles.
- Revealed an uncharacterized interaction between tobacco exposure and blood lead levels.
- Tobacco exposure showed a stronger depression of the 20th and 30th birth weight percentiles in mothers with high blood lead levels.
Conclusions:
- Quantile regression, enhanced by lasso and elastic net methods, is effective for exploring high-dimensional data in birth outcome research.
- The findings highlight a specific interaction effect of tobacco and lead exposure on lower birth weights, underscoring the need for targeted interventions.
- These advanced statistical approaches can uncover critical health insights previously hidden by mean-based analyses.
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