The analysis of environmental mixtures is of growing importance in environmental epidemiology, and one of the key goals in such analyses is to identify exposures and their interactions that are associated with adverse health outcomes. Typical approaches utilize flexible regression models combined with variable selection to identify important exposures and estimate a potentially nonlinear relationship with the outcome of interest. Despite this surge in interest, no approaches to date can identify exposures and interactions while controlling any form of error rates with respect to exposure selection. We propose two novel approaches to estimating the health effects of environmental mixtures that simultaneously 1) Estimate and provide valid inference for the overall mixture effect, and 2) identify important exposures and interactions while controlling the false discovery rate. We show that this can lead to substantial power gains to detect weak effects of environmental exposures. We apply our approaches to a study of persistent organic pollutants and find that our approach is able to identify more interactions than existing approaches.

翻译：环境混合物分析在环境流行病学中越来越重要,这种分析的关键目标之一是查明接触及其与不良健康结果有关的相互作用。典型方法采用灵活的回归模型,加上可变选择,以确定重要的接触,并估计可能与利益结果的非线性关系。尽管这种兴趣剧增,但迄今为止没有任何办法能够查明接触和相互作用,同时控制接触选择方面的任何形式的误差率。我们提出了两种新的方法,用以估计环境混合物对健康的影响,这些方法同时:(1) 估计,并为整体混合物效应提供有效的推断,和(2) 查明重要的接触和相互作用,同时控制虚假发现率。我们表明,这可以带来巨大的能量增益,以探测环境暴露的微弱影响。我们运用了我们的方法来研究持久性有机污染物,发现我们的方法比现有方法能够查明更多的相互作用。