wqsreg - A Stata command for Weighted Quantile Sum regression

Marta Ponzano, Stefano Renzetti and Andrea Bellavia
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Marta Ponzano: Department of Health Sciences, University of Genoa, Genoa, Italy
Stefano Renzetti: Department of Medicine and Surgery, University of Parma, Parma, Italy
Andrea Bellavia: Department of Environmental Health, Harvard T.H. Chan School of Public Health

Northern European Stata Conference 2025 from Stata Users Group

Abstract: Weighted Quantile Sum (WQS) regression is a flexible statistical method for quantifying the association between a set of possibly correlated predictors and a health outcome. This approach is gaining substantial popularity in several fields such as environmental epidemiology, where it allows estimating the overall effects of complex environmental mixtures as well as the specific contributions of each mixture component. A Stata command for fitting this increasingly popular procedure, however, has not been yet developed. To address this gap, we have developed a new command – wqsreg – that enables users to fit WQS regression models for continuous outcomes while allowing for the several flexible components of this framework, including: adjust for potential confounders; estimating both positive and negative overall mixture effects; providing robust weight estimates through bootstrap; specify the method used to rank variables included in the mixture (e.g. quartiles); provide iteration limits to be performed before optimization; fix the seed and customize save options. wqsreg returns the estimates from WQS regression, plots the estimated weights and creates a dataset containing the WQS index for each subject. In this talk, we will introduce the key features of WQS regression, describe wqsreg and demonstrate its use through examples. Given the increasing importance of appropriately exploring complex multidimensional exposures such as environmental mixtures, this command provides Stata users with one of the first commands to apply a modern computational approach specifically developed for these settings.

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Persistent link: https://EconPapers.repec.org/RePEc:boc:neur25:01

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