Powering Research through Innovative Methods for Mixtures in Epidemiology (PRIME) Program: Novel and Expanded Statistical Methods.
Powering Research through Innovative Methods for Mixtures in Epidemiology (PRIME) Program: Novel and Expanded Statistical Methods.
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DOI：
10.3390/ijerph19031378
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发表时间：
2022-01-26
影响因子：
--
通讯作者：
Coull BA
中科院分区：
文献类型：
--
作者：
Joubert BR;Kioumourtzoglou MA;Chamberlain T;Chen HY;Gennings C;Turyk ME;Miranda ML;Webster TF;Ensor KB;Dunson DB;Coull BA
人类在一生中暴露在各种化学和非化学暴露的混合物中。精心设计的流行病学研究以及复杂的暴露科学和相关技术使人们能够调查混合物对健康的影响。虽然现有的统计方法可以解决与环境混合物和健康终点之间的关联有关的最基本问题，但在几种常见的流行病学情景中，我们从混合物数据中学习的能力存在差距，包括空间和/或时间上的健康和暴露措施之间的高度相关性，存在观测缺失，违反重要的建模假设，以及存在当前实施所引起的计算挑战。为了应对这些和其他挑战，NIEHS发起了通过流行病学中混合物的创新方法进行动力研究(Prime)计划，以支持混合物统计方法的开发和扩展工作。Prime支持的六个独立项目卓有成效，但它们的方法尚未以一种可供应用程序参考的方式进行集中说明。我们回顾了Prime Project中的37种新方法，并总结了先前发表的研究问题的工作，以指导方法选择并提高对这些新方法的认识。我们重点介绍了在数据科学战略、暴露反应估计、暴露时间、流行病学方法、纳入毒性/化学信息、时空数据、风险评估以及模型性能、效率和解释等方面的重要统计进展。重要的是，我们链接到软件以鼓励在其他数据集上进行应用和测试。这项审查可以使环境混合物的分析更有见地。我们强调对早期职业科学家的培训以及统计方法的创新是一项持续的需要。最终，我们将努力转向减少有害接触以改善公共健康的共同目标。
Humans are exposed to a diverse mixture of chemical and non-chemical exposures across their lifetimes. Well-designed epidemiology studies as well as sophisticated exposure science and related technologies enable the investigation of the health impacts of mixtures. While existing statistical methods can address the most basic questions related to the association between environmental mixtures and health endpoints, there were gaps in our ability to learn from mixtures data in several common epidemiologic scenarios, including high correlation among health and exposure measures in space and/or time, the presence of missing observations, the violation of important modeling assumptions, and the presence of computational challenges incurred by current implementations. To address these and other challenges, NIEHS initiated the Powering Research through Innovative methods for Mixtures in Epidemiology (PRIME) program, to support work on the development and expansion of statistical methods for mixtures. Six independent projects supported by PRIME have been highly productive but their methods have not yet been described collectively in a way that would inform application. We review 37 new methods from PRIME projects and summarize the work across previously published research questions, to inform methods selection and increase awareness of these new methods. We highlight important statistical advancements considering data science strategies, exposure-response estimation, timing of exposures, epidemiological methods, the incorporation of toxicity/chemical information, spatiotemporal data, risk assessment, and model performance, efficiency, and interpretation. Importantly, we link to software to encourage application and testing on other datasets. This review can enable more informed analyses of environmental mixtures. We stress training for early career scientists as well as innovation in statistical methodology as an ongoing need. Ultimately, we direct efforts to the common goal of reducing harmful exposures to improve public health.
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影响因子：
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作者：
Ferrari F;Dunson DB
通讯作者：
Dunson DB
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