wqsreg - A Stata command for weighted quantile sum regression

Marta Ponzano, Stefano Renzetti and Andrea Bellavia
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Marta Ponzano: Department of Life Sciences, Health and Health Professions, Link Campus University, Department of Health Sciences, University of Genoa
Stefano Renzetti: Department of Medicine and Surgery, University of Parma
Andrea Bellavia: Department of Environmental Health, Harvard T.H. Chan School of Public Health, TIMI Study Group, Brigham and Women's Hospital, Harvard Medical School

Biostatistics and Epidemiology Virtual Symposium 2026 from Stata Users Group

Abstract: Weighted quantile sum (WQS) regression is a statistical method for quantifying the association between a set of possibly correlated predictors and a health outcome, estimating the joint effect of the predictors as well as their individual contributions to the total effect. We present wqsreg, the first Stata command for WQS regression, implemented for continuous, binary, and count outcomes. The execution of the command involves two sequential steps: 1) estimating the weights and constructing the WQS index under specific constraints and 2) modeling its association with the outcome. wqsreg integrates several flexible components of the framework such as bootstrap, training/validation, and repeated holdout procedures; it returns regression estimates as well as graphical displays of the individual weights. wqsreg requires Stata version 11 or higher and is freely available on GitHub. We present an application of the command on exposome data exploring the association between 38 exposures and a continuous outcome while adjusting for a set of covariates. To the best of our knowledge, wqsreg provides the first command to conduct WQS regression in Stata. We anticipate that our contribution will further promote the use of appropriate statistical methods for handling multiple correlated predictors.

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Persistent link: https://EconPapers.repec.org/RePEc:boc:biep26:03

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