Powering Research through Innovative Methods for Mixtures in Epidemiology (PRIME) Program: Novel and Expanded Statistical Methods.
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| Title: | Powering Research through Innovative Methods for Mixtures in Epidemiology (PRIME) Program: Novel and Expanded Statistical Methods. |
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| Authors: | Joubert BR; Division of Extramural Research and Training, National Institute of Environmental Health Sciences, National Institutes of Health, Durham, NC 27709, USA., Kioumourtzoglou MA; Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY 10032, USA., Chamberlain T; Division of Extramural Research and Training, National Institute of Environmental Health Sciences, National Institutes of Health, Durham, NC 27709, USA., Chen HY; Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois Chicago, Chicago, IL 60612, USA., Gennings C; Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA., Turyk ME; Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois Chicago, Chicago, IL 60612, USA., Miranda ML; Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, South Bend, IN 46556, USA., Webster TF; Department of Environmental Health, Boston University School of Public Health, Boston, MA 02118, USA., Ensor KB; Department of Statistics, Rice University, Houston, TX 77005, USA., Dunson DB; Department of Statistical Science, Duke University, Durham, NC 27710, USA., Coull BA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA. |
| Source: | International journal of environmental research and public health [Int J Environ Res Public Health] 2022 Jan 26; Vol. 19 (3). Date of Electronic Publication: 2022 Jan 26. |
| Publication Type: | Journal Article; Research Support, N.I.H., Extramural; Review |
| Language: | English |
| Journal Info: | Publisher: MDPI Country of Publication: Switzerland NLM ID: 101238455 Publication Model: Electronic Cited Medium: Internet ISSN: 1660-4601 (Electronic) Linking ISSN: 16604601 NLM ISO Abbreviation: Int J Environ Res Public Health Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Basel : MDPI, c2004- |
| MeSH Terms: | National Institute of Environmental Health Sciences (U.S.)* , Research Design*, Environmental Exposure/analysis ; Epidemiologic Methods ; Epidemiologic Studies ; Humans ; Risk Assessment ; United States |
| Abstract: | 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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| Grant Information: | R01ES028800 United States ES NIEHS NIH HHS; P30 ES023515 United States ES NIEHS NIH HHS; R01ES028819 United States ES NIEHS NIH HHS; R01 ES028790 United States ES NIEHS NIH HHS; R01ES028811 United States ES NIEHS NIH HHS; R01ES028790 United States ES NIEHS NIH HHS; R01 ES028811 United States ES NIEHS NIH HHS; R01ES028804 United States ES NIEHS NIH HHS; R01 ES028819 United States ES NIEHS NIH HHS; R01ES028805 United States ES NIEHS NIH HHS |
| Contributed Indexing: | Keywords: chemical interactions; chemicals; combined exposures; environment; epidemiology; exposomics; health impact; methods; mixtures; non-chemical stressors; risk assessment; statistics |
| Entry Date(s): | Date Created: 20220215 Date Completed: 20220228 Latest Revision: 20240822 |
| Update Code: | 20250114 |
| PubMed Central ID: | PMC8835015 |
| DOI: | 10.3390/ijerph19031378 |
| PMID: | 35162394 |
| Database: | MEDLINE |
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Nájsť tento článok vo Web of Science | Abstract: | 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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| ISSN: | 1660-4601 |
| DOI: | 10.3390/ijerph19031378 |