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

ABSTRACT: 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 correct

SUBMITTER: Carrico C 

PROVIDER: S-EPMC6261506 | biostudies-literature | 2015 Mar

REPOSITORIES: biostudies-literature