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Complex Mixtures, Complex Analyses: an Emphasis on Interpretable Results

Complex Mixtures, Complex Analyses: an Emphasis on Interpretable Results Purpose of Review The purpose of this review is to outline the main questions in environmental mixtures research and provide a non-technical explanation of novel or advanced methods to answer these questions. Recent Findings Machine learning techniques are now being incorporated into environmental mixture research to overcome issues with traditional methods. Though some methods perform well on specific tasks, no method consistently outperforms all others in complex mixture analyses, largely because different methods were developed to answer different research questions. We discuss four main questions in environmental mixtures research: (1) Are there specific exposure patterns in the study population? (2) Which are the toxic agents in the mixture? (3) Are mixture members acting synergistically? And, (4) what is the overall effect of the mixture? Summary We emphasize the importance of robust methods and interpretable results over predictive accuracy. We encourage collaboration with computer scientists, data scientists, and biostatisticians in future mixture method development. . . . . Keywords Environmental mixtures Multi-pollutant Dimension reduction Variable selection Bayesian statistics Introduction we might detect associations between some of them and the outcome of interest due to their correlation with the We are exposed daily to numerous environmental pollut- actual “bad actor(s),” i.e., http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Current Environmental Health Reports Springer Journals

Complex Mixtures, Complex Analyses: an Emphasis on Interpretable Results

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References (67)

Publisher
Springer Journals
Copyright
Copyright © 2019 by Springer Nature Switzerland AG
Subject
Biomedicine; Pharmacology/Toxicology; Medicine/Public Health, general; Environmental Health
eISSN
2196-5412
DOI
10.1007/s40572-019-00229-5
Publisher site
See Article on Publisher Site

Abstract

Purpose of Review The purpose of this review is to outline the main questions in environmental mixtures research and provide a non-technical explanation of novel or advanced methods to answer these questions. Recent Findings Machine learning techniques are now being incorporated into environmental mixture research to overcome issues with traditional methods. Though some methods perform well on specific tasks, no method consistently outperforms all others in complex mixture analyses, largely because different methods were developed to answer different research questions. We discuss four main questions in environmental mixtures research: (1) Are there specific exposure patterns in the study population? (2) Which are the toxic agents in the mixture? (3) Are mixture members acting synergistically? And, (4) what is the overall effect of the mixture? Summary We emphasize the importance of robust methods and interpretable results over predictive accuracy. We encourage collaboration with computer scientists, data scientists, and biostatisticians in future mixture method development. . . . . Keywords Environmental mixtures Multi-pollutant Dimension reduction Variable selection Bayesian statistics Introduction we might detect associations between some of them and the outcome of interest due to their correlation with the We are exposed daily to numerous environmental pollut- actual “bad actor(s),” i.e.,

Journal

Current Environmental Health ReportsSpringer Journals

Published: May 8, 2019

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