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Assessment of Grouped Weighted Quantile Sum Regression for Modeling Chemical Mixtures and Cancer Risk

Author

Listed:
  • David C. Wheeler

    (Department of Biostatistics, School of Medicine, Virginia Commonwealth University, Richmond, VA 23298-0032, USA)

  • Salem Rustom

    (Department of Biostatistics, School of Medicine, Virginia Commonwealth University, Richmond, VA 23298-0032, USA)

  • Matthew Carli

    (Department of Biostatistics, School of Medicine, Virginia Commonwealth University, Richmond, VA 23298-0032, USA)

  • Todd P. Whitehead

    (Division of Epidemiology/Biostatistics, University of California, Berkeley School of Public Health, Berkeley, CA 94704-7394, USA)

  • Mary H. Ward

    (Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20850, USA)

  • Catherine Metayer

    (Division of Epidemiology/Biostatistics, University of California, Berkeley School of Public Health, Berkeley, CA 94704-7394, USA)

Abstract

Individuals are exposed to a large number of diverse environmental chemicals simultaneously and the evaluation of multiple chemical exposures is important for identifying cancer risk factors. The measurement of a large number of chemicals (the exposome) in epidemiologic studies is allowing for a more comprehensive assessment of cancer risk factors than was done in earlier studies that focused on only a few chemicals. Empirical evidence from epidemiologic studies shows that chemicals from different chemical classes have different magnitudes and directions of association with cancers. Given increasing data availability, there is a need for the development and assessment of statistical methods to model environmental cancer risk that considers a large number of diverse chemicals with different effects for different chemical classes. The method of grouped weighted quantile sum (GWQS) regression allows for multiple groups of chemicals to be considered in the model such that different magnitudes and directions of associations are possible for each group of chemicals. In this paper, we assessed the ability of GWQS regression to estimate exposure effects for multiple chemical groups and correctly identify important chemicals in each group using a simulation study. We compared the performance of GWQS regression with WQS regression, the least absolute shrinkage and selection operator (lasso), and the group lasso in estimating exposure effects and identifying important chemicals. The simulation study results demonstrate that GWQS is an effective method for modeling exposure to multiple groups of chemicals and compares favorably with other methods used in mixture analysis. As an application, we used GWQS regression in the California Childhood Leukemia Study (CCLS), a population-based case-control study of childhood leukemia in California to estimate exposure effects for many chemical classes while also adjusting for demographic factors. The CCLS analysis found evidence of a positive association between exposure to the herbicide dacthal and an increased risk of childhood leukemia.

Suggested Citation

  • David C. Wheeler & Salem Rustom & Matthew Carli & Todd P. Whitehead & Mary H. Ward & Catherine Metayer, 2021. "Assessment of Grouped Weighted Quantile Sum Regression for Modeling Chemical Mixtures and Cancer Risk," IJERPH, MDPI, vol. 18(2), pages 1-20, January.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:2:p:504-:d:477676
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    References listed on IDEAS

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    1. Whitehead, T.P. & Metayer, C. & Ward, M.H. & Colt, J.S. & Gunier, R.B. & Deziel, N.C. & Rappaport, S.M. & Buffler, P.A., 2014. "Persistent organic pollutants in dust from older homes: Learning from lead," American Journal of Public Health, American Public Health Association, vol. 104(7), pages 1320-1326.
    2. Goodarz Danaei & Eric L Ding & Dariush Mozaffarian & Ben Taylor & Jürgen Rehm & Christopher J L Murray & Majid Ezzati, 2009. "The Preventable Causes of Death in the United States: Comparative Risk Assessment of Dietary, Lifestyle, and Metabolic Risk Factors," PLOS Medicine, Public Library of Science, vol. 6(4), pages 1-23, April.
    3. Ming Yuan & Yi Lin, 2006. "Model selection and estimation in regression with grouped variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 49-67, February.
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    Cited by:

    1. Krista Schroeder & Levent Dumenci & David B. Sarwer & Jennie G. Noll & Kevin A. Henry & Shakira F. Suglia & Christine M. Forke & David C. Wheeler, 2022. "The Intersection of Neighborhood Environment and Adverse Childhood Experiences: Methods for Creation of a Neighborhood ACEs Index," IJERPH, MDPI, vol. 19(13), pages 1-19, June.
    2. David C. Wheeler & Salem Rustom & Matthew Carli & Todd P. Whitehead & Mary H. Ward & Catherine Metayer, 2021. "Bayesian Group Index Regression for Modeling Chemical Mixtures and Cancer Risk," IJERPH, MDPI, vol. 18(7), pages 1-19, March.
    3. Matthew Carli & Mary H. Ward & Catherine Metayer & David C. Wheeler, 2022. "Imputation of Below Detection Limit Missing Data in Chemical Mixture Analysis with Bayesian Group Index Regression," IJERPH, MDPI, vol. 19(3), pages 1-17, January.
    4. Alexis E. Zavez & Emeir M. McSorley & Alison J. Yeates & Sally W. Thurston, 2023. "A Bayesian Partial Membership Model for Multiple Exposures with Uncertain Group Memberships," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(3), pages 377-400, September.
    5. Debamita Kundu & Sungduk Kim & Mary H. Ward & Paul S. Albert, 2024. "A Comparison of Statistical Methods for Studying Interactions of Chemical Mixtures," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 16(2), pages 503-519, July.

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