Complex Mixtures, Complex Analyses: an Emphasis on Interpretable Results
- 1 June 2019
- journal article
- research article
- Published by Springer Science and Business Media LLC in Current Environmental Health Reports
- Vol. 6 (2), 53-61
- https://doi.org/10.1007/s40572-019-00229-5
Abstract
Purpose of ReviewThe 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 FindingsMachine 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?SummaryWe 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
This publication has 62 references indexed in Scilit:
- The effect of primary organic particles on emergency hospital admissions among the elderly in 3 US citiesEnvironmental Health, 2013
- A Cohort study evaluation of maternal PCB exposure related to time to pregnancy in daughtersEnvironmental Health, 2013
- Perinatal Exposure to Hazardous Air Pollutants and Autism Spectrum Disorders at Age 8Epidemiology, 2010
- Using underapproximations for sparse nonnegative matrix factorizationPattern Recognition, 2010
- Book Review: Exploratory and Confirmatory Factor Analysis: Understanding Concepts and ApplicationsApplied Psychological Measurement, 2007
- Semiparametric Regression of Multidimensional Genetic Pathway Data: Least‐Squares Kernel Machines and Linear Mixed ModelsBiometrics, 2007
- Bayesian Methods for Highly Correlated Exposure DataEpidemiology, 2007
- The 2005 World Health Organization Reevaluation of Human and Mammalian Toxic Equivalency Factors for Dioxins and Dioxin-Like CompoundsToxicological Sciences, 2006
- Regularization and Variable Selection Via the Elastic NetJournal of the Royal Statistical Society Series B: Statistical Methodology, 2005
- Ridge Regression: Biased Estimation for Nonorthogonal ProblemsTechnometrics, 1970