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Workflow for Statistical Analysis of Environmental Mixtures.

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Title: Workflow for Statistical Analysis of Environmental Mixtures.
Authors: Joubert, Bonnie R.1 bonnie.joubert@nih.gov, Palmer, Glenn2, Dunson, David2, Kioumourtzoglou, Marianthi-Anna3, Coull, Brent A.4
Source: Environmental Health Perspectives. May2026, Vol. 134 Issue 1, p8-22. 15p.
Subject Terms: *Environmental health, *Environmental monitoring, *Environmental exposure, *Pollutants, *Epidemiological research, Statistical models, Documentation, Cross-sectional method, Data analysis, Data mining, Probability theory, Multivariate analysis, Workflow, Experimental design, Longitudinal method, Surveys, Causality (Physics), Statistics, Acquisition of data, Data analysis software, Regression analysis
Abstract: BACKGROUND: Human exposure to complex, changing, and variably correlated mixtures of environmental chemicals has presented analytical challenges to epidemiologists and human health researchers. There has been a wide variety of recent advances in statistical methods for analyzing mixtures data, with most methods having open-source software for implementation. However, there is no one-size-fits-all method for analyzing mixture data given the considerable heterogeneity in scientific focus and study design. For example, some methods focus on predicting the overall health effect of a mixture and others seek to disentangle main effects and pairwise interactions. Some methods are only appropriate for cross-sectional designs, while other methods can accommodate longitudinally measured exposures or outcomes. OBJECTIVES: This article focuses on simplifying the task of identifying which methods are most appropriate to a particular study design, data type, and scientific focus. METHODS: We present an organized workflow for statistical analysis considerations in environmental mixtures data and two example applications implementing the workflow. This systematic strategy builds on epidemiological and statistical principles, considering specific nuances for the mixtures’ context. We also present an accompanying methods repository to increase awareness of and inform application of existing methods and new methods as they are developed. DISCUSSION: We note several methods may be equally appropriate for a specific context. This article does not present a comparison or contrast of methods or recommend one method over another. Rather, the presented workflow can be used to identify a set of methods that are appropriate for a given application. Accordingly, this effort will inform application, educate researchers (e.g., new researchers or trainees), and identify research gaps in statistical methods for environmental mixtures that warrant further development [ABSTRACT FROM AUTHOR]
Copyright of Environmental Health Perspectives is the property of National Institute of Environmental Health Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Workflow for Statistical Analysis of Environmental Mixtures.
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  Data: <searchLink fieldCode="AR" term="%22Joubert%2C+Bonnie+R%2E%22">Joubert, Bonnie R.</searchLink><relatesTo>1</relatesTo><i> bonnie.joubert@nih.gov</i><br /><searchLink fieldCode="AR" term="%22Palmer%2C+Glenn%22">Palmer, Glenn</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Dunson%2C+David%22">Dunson, David</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Kioumourtzoglou%2C+Marianthi-Anna%22">Kioumourtzoglou, Marianthi-Anna</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Coull%2C+Brent+A%2E%22">Coull, Brent A.</searchLink><relatesTo>4</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Environmental+Health+Perspectives%22">Environmental Health Perspectives</searchLink>. May2026, Vol. 134 Issue 1, p8-22. 15p.
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  Data: *<searchLink fieldCode="DE" term="%22Environmental+health%22">Environmental health</searchLink><br />*<searchLink fieldCode="DE" term="%22Environmental+monitoring%22">Environmental monitoring</searchLink><br />*<searchLink fieldCode="DE" term="%22Environmental+exposure%22">Environmental exposure</searchLink><br />*<searchLink fieldCode="DE" term="%22Pollutants%22">Pollutants</searchLink><br />*<searchLink fieldCode="DE" term="%22Epidemiological+research%22">Epidemiological research</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+models%22">Statistical models</searchLink><br /><searchLink fieldCode="DE" term="%22Documentation%22">Documentation</searchLink><br /><searchLink fieldCode="DE" term="%22Cross-sectional+method%22">Cross-sectional method</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Probability+theory%22">Probability theory</searchLink><br /><searchLink fieldCode="DE" term="%22Multivariate+analysis%22">Multivariate analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Workflow%22">Workflow</searchLink><br /><searchLink fieldCode="DE" term="%22Experimental+design%22">Experimental design</searchLink><br /><searchLink fieldCode="DE" term="%22Longitudinal+method%22">Longitudinal method</searchLink><br /><searchLink fieldCode="DE" term="%22Surveys%22">Surveys</searchLink><br /><searchLink fieldCode="DE" term="%22Causality+%28Physics%29%22">Causality (Physics)</searchLink><br /><searchLink fieldCode="DE" term="%22Statistics%22">Statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Acquisition+of+data%22">Acquisition of data</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis+software%22">Data analysis software</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+analysis%22">Regression analysis</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: BACKGROUND: Human exposure to complex, changing, and variably correlated mixtures of environmental chemicals has presented analytical challenges to epidemiologists and human health researchers. There has been a wide variety of recent advances in statistical methods for analyzing mixtures data, with most methods having open-source software for implementation. However, there is no one-size-fits-all method for analyzing mixture data given the considerable heterogeneity in scientific focus and study design. For example, some methods focus on predicting the overall health effect of a mixture and others seek to disentangle main effects and pairwise interactions. Some methods are only appropriate for cross-sectional designs, while other methods can accommodate longitudinally measured exposures or outcomes. OBJECTIVES: This article focuses on simplifying the task of identifying which methods are most appropriate to a particular study design, data type, and scientific focus. METHODS: We present an organized workflow for statistical analysis considerations in environmental mixtures data and two example applications implementing the workflow. This systematic strategy builds on epidemiological and statistical principles, considering specific nuances for the mixtures’ context. We also present an accompanying methods repository to increase awareness of and inform application of existing methods and new methods as they are developed. DISCUSSION: We note several methods may be equally appropriate for a specific context. This article does not present a comparison or contrast of methods or recommend one method over another. Rather, the presented workflow can be used to identify a set of methods that are appropriate for a given application. Accordingly, this effort will inform application, educate researchers (e.g., new researchers or trainees), and identify research gaps in statistical methods for environmental mixtures that warrant further development [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Environmental Health Perspectives is the property of National Institute of Environmental Health Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1021/EHP.6c00155
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 15
        StartPage: 8
    Subjects:
      – SubjectFull: Environmental health
        Type: general
      – SubjectFull: Environmental monitoring
        Type: general
      – SubjectFull: Environmental exposure
        Type: general
      – SubjectFull: Pollutants
        Type: general
      – SubjectFull: Epidemiological research
        Type: general
      – SubjectFull: Statistical models
        Type: general
      – SubjectFull: Documentation
        Type: general
      – SubjectFull: Cross-sectional method
        Type: general
      – SubjectFull: Data analysis
        Type: general
      – SubjectFull: Data mining
        Type: general
      – SubjectFull: Probability theory
        Type: general
      – SubjectFull: Multivariate analysis
        Type: general
      – SubjectFull: Workflow
        Type: general
      – SubjectFull: Experimental design
        Type: general
      – SubjectFull: Longitudinal method
        Type: general
      – SubjectFull: Surveys
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      – SubjectFull: Causality (Physics)
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      – SubjectFull: Statistics
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      – SubjectFull: Acquisition of data
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      – SubjectFull: Data analysis software
        Type: general
      – SubjectFull: Regression analysis
        Type: general
    Titles:
      – TitleFull: Workflow for Statistical Analysis of Environmental Mixtures.
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              M: 05
              Text: May2026
              Type: published
              Y: 2026
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