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Statistical Approaches for Assessing Health Effects of Environmental Chemical Mixtures in Epidemiology: Lessons from an Innovative Workshop
Author(s) -
Kyla W. Taylor,
Bonnie R. Joubert,
Joe M. Braun,
Caroline Dilworth,
Chris Gennings,
Russ Hauser,
Jerry Heindel,
Cynthia V. Rider,
Thomas F. Webster,
Danielle J. Carlin
Publication year - 2016
Publication title -
environmental health perspectives
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.257
H-Index - 282
eISSN - 1552-9924
pISSN - 0091-6765
DOI - 10.1289/ehp547
Subject(s) - environmental epidemiology , data science , epidemiology , statistical model , public health , biostatistics , statistical analysis , computer science , management science , environmental health , data mining , medicine , statistics , mathematics , machine learning , engineering , nursing
Summary: Quantifying the impact of exposure to environmental chemical mixtures is important for identifying risk factors for diseases and developing more targeted public health interventions. The National Institute of Environmental Health Sciences (NIEHS) held a workshop in July 2015 to address the need to develop novel statistical approaches for multi-pollutant epidemiology studies. The primary objective of the workshop was to identify and compare different statistical approaches and methods for analyzing complex chemical mixtures data in both simulated and real-world data sets. At the workshop, participants compared approaches and results and speculated as to why they may have differed. Several themes emerged: a) no one statistical approach appeared to outperform the others, b) many methods included some form of variable reduction or summation of the data before statistical analysis, c) the statistical approach should be selected based upon a specific hypothesis or scientific question, and d) related mixtures data should be shared among researchers to more comprehensively and accurately address methodological questions and statistical approaches. Future efforts should continue to design and optimize statistical approaches to address questions about chemical mixtures in epidemiological studies.

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