Powering Research through Innovative Methods for Mixtures in Epidemiology (PRIME) Program: Novel and Expanded Statistical Methods.

Journal Article (Review;Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Joubert, BR; Kioumourtzoglou, M-A; Chamberlain, T; Chen, HY; Gennings, C; Turyk, ME; Miranda, ML; Webster, TF; Ensor, KB; Dunson, DB; Coull, BA

Published Date

  • January 2022

Published In

Volume / Issue

  • 19 / 3

Start / End Page

  • 1378 -

PubMed ID

  • 35162394

Pubmed Central ID

  • PMC8835015

Electronic International Standard Serial Number (EISSN)

  • 1660-4601

International Standard Serial Number (ISSN)

  • 1661-7827

Digital Object Identifier (DOI)

  • 10.3390/ijerph19031378

Language

  • eng