A random subset implementation of weighted quantile sum (WQS_RS) regression for analysis of high-dimensional mixtures

Here we introduce a novel implementation of weighted quantile sum (WQS) regression, a modeling strategy for mixtures analyses, which integrates a random subset algorithm in the estimation of mixture effects. We demonstrate the application of this method (WQS_RS) in three case examples, with mixtures varying in size from 34 to 472 variables. In evaluating each case, we provide detailed simulation studies to characterize the sensitivity and specificity of WQS_RS in varying contexts. Our results emphasize that WQS_RS is robustly effective in evaluating mixture effects in diverse high-dimensional contexts, yielding sensitivity and specificity in empirical contexts of approximately 73–75% and 73–89%, respectively.