Characterization of Weighted Quantile Sum Regression for Highly Correlated Data in a Risk Analysis Setting

By C. Carrico, Chris Gennings, David C. Wheeler, P. Factor-Litvak. Journal of Agricultural Biological and Environmental Statistics, 2014

1,160 citations recorded by the source corpus.

In risk evaluation, the effect of mixtures of environmental chemicals on a common adverse outcome is of interest. However, due to the high dimensionality and inherent correlations among chemicals that occur together, the traditional methods (e.g. ordinary or logistic regression) suffer from collinearity and variance inflation, and shrinkage methods have limitations in selecting among correlated components. We propose a weighted quantile sum (WQS) approach to estimating a body burden index, which identifies “bad actors” in a set of highly correlated environmental chemicals. We evaluate and characterize the accuracy of WQS regression in variable selection through extensive simulation studies through sensitivity and specificity (i.e., ability of the WQS method to select the bad actors correctly and not incorrect ones). We demonstrate the improvement in accuracy this method provides over traditional ordinary regression and shrinkage methods (lasso, adaptive lasso, and elastic net). Results from simulations demonstrate that WQS regression is accurate under some environmentally relevant conditions, but its accuracy decreases for a fixed correlation pattern as the association with a response variable diminishes. Nonzero weights (i.e., weights exceeding a selection threshold parameter) may be used to identify bad actors; however, components within a cluster of highly correlated active components tend to have lower weights, with the sum of their weights representative of the set. Supplementary materials accompanying this paper appear on-line.

Subfields: Health, Toxicology and Mutagenesis, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/00f3c2cc14ee04278f3f40e0997fd931b342d220

Citations in the corpus