Assessment of weighted quantile sum regression for modelling chemical mixtures and cancer risk

In evaluation of cancer risk related to environmental chemical exposures, the effect of many chemicals on disease is ultimately of interest. However, because of potentially strong correlations among chemicals that occur together, traditional regression methods suffer from collinearity effects, including regression coefficient sign reversal and variance inflation. In addition, penalised regression methods designed to remediate collinearity may have limitations in selecting the truly bad actors among many correlated components. The recently proposed method of weighted quantile sum (WQS) regression attempts to overcome these problems by estimating a body burden index, which identifies important chemicals in a mixture of correlated environmental chemicals. The authors focus was on assessing through simulation studies the accuracy of WQS regression in detecting subsets of chemicals associated with health outcomes (binary and continuous) in site-specific analyses and in non-site-specific analyses. In addition, the performance of the penalised regression methods of lasso, adaptive lasso, and elastic net were evaluated in correctly classifying chemicals as bad actors or unrelated to the outcome. The simulation study was based on data from the National Cancer Institute Surveillance Epidemiology and End Results Program (NCI-SEER) case-control study of non-Hodgkin lymphoma (NHL) to achieve realistic exposure situations. The results showed that WQS regression had good sensitivity and specificity across a variety of conditions considered in this study. The shrinkage methods had a tendency to incorrectly identify a large number of components, especially in the case of strong association with the outcome.

Authors: Czarnota J, Gennings C, Wheeler DC. ;Full Source: Cancer Inform. 2015 May 13;14(Suppl 2):159-71. doi: 10.4137/CIN.S17295. eCollection 2015. ;