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  1. Home
  3. Wqsreg: A Stata Command For Weighted Quantile Sum Regression.
  1. Home
  3. Wqsreg: A Stata Command For Weighted Quantile Sum Regression.

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Published on: January 8, 2020

Wqsreg: a Stata command for weighted quantile sum regression.

Marta Ponzano1,2, Stefano Renzetti3, Chris Gennings4

  • 1Department of Life Sciences, Health and Health Professions, Link Campus University, Rome, Italy.

European Journal of Epidemiology
|June 25, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Weighted Quantile Sum (WQS) regression, a method for analyzing environmental exposures, is now available in Stata with the new wqsreg command. This tool helps researchers understand the combined and individual effects of multiple correlated predictors on health outcomes.

Keywords:
BiomarkersCorrelated predictorsEnvironmental mixturesSoftwareStata

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Area of Science:

  • Environmental Epidemiology
  • Biostatistics
  • Statistical Genetics

Background:

  • Weighted Quantile Sum (WQS) regression is a key method for analyzing complex mixtures in environmental epidemiology.
  • Its application has been limited due to a lack of accessible software outside the R environment.
  • Addressing this gap is crucial for advancing research on multidimensional exposures.

Purpose of the Study:

  • Introduce `wqsreg`, the first Stata command for Weighted Quantile Sum (WQS) regression.
  • Provide a user-friendly implementation of WQS regression for continuous, binary, and count outcomes.
  • Facilitate the analysis of complex mixtures in epidemiological studies.

Main Methods:

  • Developed `wqsreg`, a Stata command for WQS regression.
  • Implemented features such as bootstrap, training/validation splitting, and repeated holdout procedures.
  • Demonstrated the command's utility with exposome data, analyzing 38 exposures and a continuous outcome.

    • Main Results:

    • `wqsreg` offers a flexible and user-friendly approach to WQS regression in Stata.
    • The command successfully quantified associations between multiple environmental exposures and a health outcome.
    • Graphical displays of individual weights provide insights into predictor contributions.

    Conclusions:

    • The `wqsreg` command expands access to WQS regression for Stata users in environmental epidemiology.
    • This tool supports the investigation of complex mixtures and multidimensional exposures.
    • Promotes the use of advanced statistical methods for analyzing correlated predictors in health research.