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

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...

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Autores principales: Joubert, Bonnie R., Kioumourtzoglou, Marianthi-Anna, Chamberlain, Toccara, Chen, Hua Yun, Gennings, Chris, Turyk, Mary E., Miranda, Marie Lynn, Webster, Thomas F., Ensor, Katherine B., Dunson, David B., Coull, Brent A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8835015/
https://www.ncbi.nlm.nih.gov/pubmed/35162394
http://dx.doi.org/10.3390/ijerph19031378
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author Joubert, Bonnie R.
Kioumourtzoglou, Marianthi-Anna
Chamberlain, Toccara
Chen, Hua Yun
Gennings, Chris
Turyk, Mary E.
Miranda, Marie Lynn
Webster, Thomas F.
Ensor, Katherine B.
Dunson, David B.
Coull, Brent A.
author_facet Joubert, Bonnie R.
Kioumourtzoglou, Marianthi-Anna
Chamberlain, Toccara
Chen, Hua Yun
Gennings, Chris
Turyk, Mary E.
Miranda, Marie Lynn
Webster, Thomas F.
Ensor, Katherine B.
Dunson, David B.
Coull, Brent A.
author_sort Joubert, Bonnie R.
collection PubMed
description 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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spelling pubmed-88350152022-02-12 Powering Research through Innovative Methods for Mixtures in Epidemiology (PRIME) Program: Novel and Expanded Statistical Methods Joubert, Bonnie R. Kioumourtzoglou, Marianthi-Anna Chamberlain, Toccara Chen, Hua Yun Gennings, Chris Turyk, Mary E. Miranda, Marie Lynn Webster, Thomas F. Ensor, Katherine B. Dunson, David B. Coull, Brent A. Int J Environ Res Public Health Review 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. MDPI 2022-01-26 /pmc/articles/PMC8835015/ /pubmed/35162394 http://dx.doi.org/10.3390/ijerph19031378 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Joubert, Bonnie R.
Kioumourtzoglou, Marianthi-Anna
Chamberlain, Toccara
Chen, Hua Yun
Gennings, Chris
Turyk, Mary E.
Miranda, Marie Lynn
Webster, Thomas F.
Ensor, Katherine B.
Dunson, David B.
Coull, Brent A.
Powering Research through Innovative Methods for Mixtures in Epidemiology (PRIME) Program: Novel and Expanded Statistical Methods
title Powering Research through Innovative Methods for Mixtures in Epidemiology (PRIME) Program: Novel and Expanded Statistical Methods
title_full Powering Research through Innovative Methods for Mixtures in Epidemiology (PRIME) Program: Novel and Expanded Statistical Methods
title_fullStr Powering Research through Innovative Methods for Mixtures in Epidemiology (PRIME) Program: Novel and Expanded Statistical Methods
title_full_unstemmed Powering Research through Innovative Methods for Mixtures in Epidemiology (PRIME) Program: Novel and Expanded Statistical Methods
title_short Powering Research through Innovative Methods for Mixtures in Epidemiology (PRIME) Program: Novel and Expanded Statistical Methods
title_sort powering research through innovative methods for mixtures in epidemiology (prime) program: novel and expanded statistical methods
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8835015/
https://www.ncbi.nlm.nih.gov/pubmed/35162394
http://dx.doi.org/10.3390/ijerph19031378
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