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Bayesian Weighted Sums: A Flexible Approach to Estimate Summed Mixture Effects

Objectives: Methods exist to study exposure mixtures, but each is distinct in the research question it aims to address. We propose a new approach focused on estimating both the summed effect and individual weights of one or multiple exposure mixtures: Bayesian Weighted Sums (BWS). Methods: We applie...

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Autores principales: Hamra, Ghassan B., Maclehose, Richard F., Croen, Lisa, Kauffman, Elizabeth M., Newschaffer, Craig
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7913173/
https://www.ncbi.nlm.nih.gov/pubmed/33546139
http://dx.doi.org/10.3390/ijerph18041373
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author Hamra, Ghassan B.
Maclehose, Richard F.
Croen, Lisa
Kauffman, Elizabeth M.
Newschaffer, Craig
author_facet Hamra, Ghassan B.
Maclehose, Richard F.
Croen, Lisa
Kauffman, Elizabeth M.
Newschaffer, Craig
author_sort Hamra, Ghassan B.
collection PubMed
description Objectives: Methods exist to study exposure mixtures, but each is distinct in the research question it aims to address. We propose a new approach focused on estimating both the summed effect and individual weights of one or multiple exposure mixtures: Bayesian Weighted Sums (BWS). Methods: We applied BWS to simulated and real datasets with correlated exposures. The analytic context in our real-world example is an estimation of the association between polybrominated diphenyl ether (PBDE) congeners (28, 47, 99, 100, and 153) and Autism Spectrum Disorder (ASD) diagnosis and Social Responsiveness Scores (SRS). Results: Simulations demonstrate that BWS performs reliably. In adjusted models using Early Autism Risk Longitudinal Investigation (EARLI) data, the odds of ASD for a 1-unit increase in the weighted sum of PBDEs were 1.41 (95% highest posterior density 0.82, 2.50) times the odds of ASD for the unexposed and the change in z-score standardized SRS per 1 unit increase in the weighted sum of PBDEs is 0.15 (95% highest posterior density −0.08, 0.38). Conclusions: BWS provides a means of estimating the summed effect and weights for individual components of a mixture. This approach is distinct from other exposure mixture tools. BWS may be more flexible than existing approaches and can be specified to allow multiple exposure groups based on a priori knowledge from epidemiology or toxicology.
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spelling pubmed-79131732021-02-28 Bayesian Weighted Sums: A Flexible Approach to Estimate Summed Mixture Effects Hamra, Ghassan B. Maclehose, Richard F. Croen, Lisa Kauffman, Elizabeth M. Newschaffer, Craig Int J Environ Res Public Health Article Objectives: Methods exist to study exposure mixtures, but each is distinct in the research question it aims to address. We propose a new approach focused on estimating both the summed effect and individual weights of one or multiple exposure mixtures: Bayesian Weighted Sums (BWS). Methods: We applied BWS to simulated and real datasets with correlated exposures. The analytic context in our real-world example is an estimation of the association between polybrominated diphenyl ether (PBDE) congeners (28, 47, 99, 100, and 153) and Autism Spectrum Disorder (ASD) diagnosis and Social Responsiveness Scores (SRS). Results: Simulations demonstrate that BWS performs reliably. In adjusted models using Early Autism Risk Longitudinal Investigation (EARLI) data, the odds of ASD for a 1-unit increase in the weighted sum of PBDEs were 1.41 (95% highest posterior density 0.82, 2.50) times the odds of ASD for the unexposed and the change in z-score standardized SRS per 1 unit increase in the weighted sum of PBDEs is 0.15 (95% highest posterior density −0.08, 0.38). Conclusions: BWS provides a means of estimating the summed effect and weights for individual components of a mixture. This approach is distinct from other exposure mixture tools. BWS may be more flexible than existing approaches and can be specified to allow multiple exposure groups based on a priori knowledge from epidemiology or toxicology. MDPI 2021-02-03 2021-02 /pmc/articles/PMC7913173/ /pubmed/33546139 http://dx.doi.org/10.3390/ijerph18041373 Text en © 2021 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hamra, Ghassan B.
Maclehose, Richard F.
Croen, Lisa
Kauffman, Elizabeth M.
Newschaffer, Craig
Bayesian Weighted Sums: A Flexible Approach to Estimate Summed Mixture Effects
title Bayesian Weighted Sums: A Flexible Approach to Estimate Summed Mixture Effects
title_full Bayesian Weighted Sums: A Flexible Approach to Estimate Summed Mixture Effects
title_fullStr Bayesian Weighted Sums: A Flexible Approach to Estimate Summed Mixture Effects
title_full_unstemmed Bayesian Weighted Sums: A Flexible Approach to Estimate Summed Mixture Effects
title_short Bayesian Weighted Sums: A Flexible Approach to Estimate Summed Mixture Effects
title_sort bayesian weighted sums: a flexible approach to estimate summed mixture effects
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7913173/
https://www.ncbi.nlm.nih.gov/pubmed/33546139
http://dx.doi.org/10.3390/ijerph18041373
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