Wqsreg: a Stata command for weighted quantile sum regression

Weighted Quantile Sum (WQS) regression is a statistical method for quantifying the association between multiple possibly correlated predictors and a health outcome, estimating both the joint effect of the predictors as well as their individual contributions to the total effect. WQS has become one of the most popular and widely used approaches for investigating complex mixtures in environmental epidemiology, yet its implementation has been largely restricted to R users. In this paper we present wqsreg, the first Stata command for WQS regression, implemented for continuous, binary and count outcomes. We describe command's architecture and present an application of the command on exposome data exploring the association between 38 exposures and a continuous outcome. Wqsreg provides a user-friendly command for WQS regression that integrates several flexible components of the framework such as bootstrap, training/validation splitting, and repeated holdout procedures. Wqsreg returns regression estimates as well as graphical displays of the individual weights. It requires Stata version 11 or higher and is freely available on GitHub [ https://github.com/PonzanoMarta/wqsreg ]. Given the increasing importance of appropriately exploring complex multidimensional exposures, this contribution will further promote the use of appropriate statistical methods in epidemiological settings with multiple correlated predictors.