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Powering Research through Innovative Methods for Mixtures in Epidemiology (PRIME) Program: Novel and Expanded Statistical Methods

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  • Bonnie R. Joubert

    (Division of Extramural Research and Training, National Institute of Environmental Health Sciences, National Institutes of Health, Durham, NC 27709, USA)

  • Marianthi-Anna Kioumourtzoglou

    (Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY 10032, USA)

  • Toccara Chamberlain

    (Division of Extramural Research and Training, National Institute of Environmental Health Sciences, National Institutes of Health, Durham, NC 27709, USA)

  • Hua Yun Chen

    (Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois Chicago, Chicago, IL 60612, USA)

  • Chris Gennings

    (Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA)

  • Mary E. Turyk

    (Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois Chicago, Chicago, IL 60612, USA)

  • Marie Lynn Miranda

    (Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, South Bend, IN 46556, USA)

  • Thomas F. Webster

    (Department of Environmental Health, Boston University School of Public Health, Boston, MA 02118, USA)

  • Katherine B. Ensor

    (Department of Statistics, Rice University, Houston, TX 77005, USA)

  • David B. Dunson

    (Department of Statistical Science, Duke University, Durham, NC 27710, USA)

  • Brent A. Coull

    (Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA)

Abstract

Humans are exposed to a diverse mixture of chemical and non-chemical exposures across their lifetimes. Well-designed epidemiology studies as well as sophisticated exposure science and related technologies enable the investigation of the health impacts of mixtures. While existing statistical methods can address the most basic questions related to the association between environmental mixtures and health endpoints, there were gaps in our ability to learn from mixtures data in several common epidemiologic scenarios, including high correlation among health and exposure measures in space and/or time, the presence of missing observations, the violation of important modeling assumptions, and the presence of computational challenges incurred by current implementations. To address these and other challenges, NIEHS initiated the Powering Research through Innovative methods for Mixtures in Epidemiology (PRIME) program, to support work on the development and expansion of statistical methods for mixtures. Six independent projects supported by PRIME have been highly productive but their methods have not yet been described collectively in a way that would inform application. We review 37 new methods from PRIME projects and summarize the work across previously published research questions, to inform methods selection and increase awareness of these new methods. We highlight important statistical advancements considering data science strategies, exposure-response estimation, timing of exposures, epidemiological methods, the incorporation of toxicity/chemical information, spatiotemporal data, risk assessment, and model performance, efficiency, and interpretation. Importantly, we link to software to encourage application and testing on other datasets. This review can enable more informed analyses of environmental mixtures. We stress training for early career scientists as well as innovation in statistical methodology as an ongoing need. Ultimately, we direct efforts to the common goal of reducing harmful exposures to improve public health.

Suggested Citation

  • Bonnie R. Joubert & Marianthi-Anna Kioumourtzoglou & Toccara Chamberlain & Hua Yun Chen & Chris Gennings & Mary E. Turyk & Marie Lynn Miranda & Thomas F. Webster & Katherine B. Ensor & David B. Dunson, 2022. "Powering Research through Innovative Methods for Mixtures in Epidemiology (PRIME) Program: Novel and Expanded Statistical Methods," IJERPH, MDPI, vol. 19(3), pages 1-24, January.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:3:p:1378-:d:734560
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    References listed on IDEAS

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    1. Hua Yun Chen & Hesen Li & Maria Argos & Victoria W. Persky & Mary E. Turyk, 2022. "Statistical Methods for Assessing the Explained Variation of a Health Outcome by a Mixture of Exposures," IJERPH, MDPI, vol. 19(5), pages 1-16, February.
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    5. Lucas Janson & Rina Foygel Barber & Emmanuel Candès, 2017. "EigenPrism: inference for high dimensional signal-to-noise ratios," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(4), pages 1037-1065, September.
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    1. Laura A. Rodriguez-Villamizar & Olga M. Medina & Oscar Flórez-Vargas & Eugenio Vilanova & Alvaro J. Idrovo & Santiago A. Araque-Rodriguez & José A. Henao & Luz H. Sánchez-Rodríguez, 2023. "Chemical Element Mixtures and Kidney Function in Mining and Non-Mining Settings in Northern Colombia," IJERPH, MDPI, vol. 20(3), pages 1-15, January.

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