环境流行病学
数据科学
流行病学
统计模型
公共卫生
生物统计学
统计分析
计算机科学
管理科学
环境卫生
数据挖掘
医学
统计
数学
机器学习
工程类
内科学
护理部
作者
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
摘要
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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