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Borrowing strength across exposures and outcomes via index models for multi-pollutant mixtures.

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Title: Borrowing strength across exposures and outcomes via index models for multi-pollutant mixtures.
Authors: McGee G; Department of Statistics and Actuarial Science, University of Waterloo, ON, N2L 3G1, Canada., Antonelli J; Department of Statistics, University of Florida, FL, 32611, United States.
Source: Biometrics [Biometrics] 2026 Apr 09; Vol. 82 (2).
Publication Type: Journal Article
Language: English
Journal Info: Publisher: Oxford University Press Country of Publication: England NLM ID: 0370625 Publication Model: Print Cited Medium: Internet ISSN: 1541-0420 (Electronic) Linking ISSN: 0006341X NLM ISO Abbreviation: Biometrics Subsets: MEDLINE
Imprint Name(s): Publication: March 2024- : [Oxford] : Oxford University Press
Original Publication: Alexandria Va : Biometric Society
MeSH Terms: Environmental Exposure*/statistics & numerical data , Air Pollutants*/analysis , Models, Statistical* , Complex Mixtures*, Air Pollution/statistics & numerical data ; Biometry/methods ; Humans ; Bayes Theorem ; Computer Simulation
Abstract: An important goal of environmental health research is to assess the health risks posed by mixtures of multiple environmental exposures. In these mixtures analyses, flexible models such as Bayesian kernel machine regression and multiple index models are appealing because they allow for arbitrary non-linear exposure-outcome relationships. However, this flexibility comes at the cost of low power, particularly when exposures are highly correlated and the health effects are weak, as is typical in environmental health studies. We propose a multivariate index modeling strategy that borrows strength across exposures and outcomes by exploiting similar mixture component weights and exposure-response relationships. In the special case of distributed lag models, in which exposures are measured repeatedly over time, we jointly encourage co-clustering of lag profiles and exposure-response curves to more efficiently identify critical windows of vulnerability and characterize important exposure effects. We then extend the proposed approach to the multiple index model setting where the true index structure-the number of indices and their composition-is unknown, and introduce variable importance measures to quantify component contributions to mixture effects. Using time series data from the National Morbidity, Mortality and Air Pollution Study, we demonstrate the proposed methods by jointly modeling three mortality outcomes and two cumulative air pollution measurements with a maximum lag of 14 days.
(© The Author(s) 2026. Published by Oxford University Press on behalf of The International Biometric Society.)
Grant Information: RGPIN-2022-03068 Natural Sciences and Engineering Research Council of Canada; DGECR-2022-00433 Natural Sciences and Engineering Research Council of Canada
Contributed Indexing: Keywords: Dirichlet process mixtures; distributed lag models; multiple index models; multipollutant mixtures
Substance Nomenclature: 0 (Air Pollutants)
0 (Complex Mixtures)
Entry Date(s): Date Created: 20260513 Date Completed: 20260716 Latest Revision: 20260716
Update Code: 20260717
DOI: 10.1093/biomtc/ujag072
PMID: 42127285
Database: MEDLINE
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  Data: Borrowing strength across exposures and outcomes via index models for multi-pollutant mixtures.
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  Data: <searchLink fieldCode="AU" term="%22McGee+G%22">McGee G</searchLink>; Department of Statistics and Actuarial Science, University of Waterloo, ON, N2L 3G1, Canada.<br /><searchLink fieldCode="AU" term="%22Antonelli+J%22">Antonelli J</searchLink>; Department of Statistics, University of Florida, FL, 32611, United States.
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  Data: <i>Publication</i>: March 2024- : [Oxford] : Oxford University Press<br /><i>Original Publication</i>: Alexandria Va : Biometric Society
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  Data: <searchLink fieldCode="MM" term="%22Environmental+Exposure%22">Environmental Exposure*</searchLink>/<searchLink fieldCode="MM" term="%22Environmental+Exposure+statistics+%26+numerical+data%22">statistics & numerical data</searchLink> <br /><searchLink fieldCode="MM" term="%22Air+Pollutants%22">Air Pollutants*</searchLink>/<searchLink fieldCode="MM" term="%22Air+Pollutants+analysis%22">analysis</searchLink> <br /><searchLink fieldCode="MM" term="%22Models%2C+Statistical%22">Models, Statistical*</searchLink> <br /><searchLink fieldCode="MM" term="%22Complex+Mixtures%22">Complex Mixtures*</searchLink><br /><searchLink fieldCode="MH" term="%22Air+Pollution%22">Air Pollution</searchLink>/<searchLink fieldCode="MH" term="%22Air+Pollution+statistics+%26+numerical+data%22">statistics & numerical data</searchLink> ; <searchLink fieldCode="MH" term="%22Biometry%22">Biometry</searchLink>/<searchLink fieldCode="MH" term="%22Biometry+methods%22">methods</searchLink> ; <searchLink fieldCode="MH" term="%22Humans%22">Humans</searchLink> ; <searchLink fieldCode="MH" term="%22Bayes+Theorem%22">Bayes Theorem</searchLink> ; <searchLink fieldCode="MH" term="%22Computer+Simulation%22">Computer Simulation</searchLink>
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  Data: An important goal of environmental health research is to assess the health risks posed by mixtures of multiple environmental exposures. In these mixtures analyses, flexible models such as Bayesian kernel machine regression and multiple index models are appealing because they allow for arbitrary non-linear exposure-outcome relationships. However, this flexibility comes at the cost of low power, particularly when exposures are highly correlated and the health effects are weak, as is typical in environmental health studies. We propose a multivariate index modeling strategy that borrows strength across exposures and outcomes by exploiting similar mixture component weights and exposure-response relationships. In the special case of distributed lag models, in which exposures are measured repeatedly over time, we jointly encourage co-clustering of lag profiles and exposure-response curves to more efficiently identify critical windows of vulnerability and characterize important exposure effects. We then extend the proposed approach to the multiple index model setting where the true index structure-the number of indices and their composition-is unknown, and introduce variable importance measures to quantify component contributions to mixture effects. Using time series data from the National Morbidity, Mortality and Air Pollution Study, we demonstrate the proposed methods by jointly modeling three mortality outcomes and two cumulative air pollution measurements with a maximum lag of 14 days.<br /> (© The Author(s) 2026. Published by Oxford University Press on behalf of The International Biometric Society.)
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  Data: <i>Keywords: </i>Dirichlet process mixtures; distributed lag models; multiple index models; multipollutant mixtures
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        Value: 10.1093/biomtc/ujag072
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        Text: English
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      – SubjectFull: Air Pollution statistics & numerical data
        Type: general
      – SubjectFull: Biometry methods
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              Text: 2026 Apr 09
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