Bayesian ensemble learning for predicting health outcomes of multipollutant mixtures
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912389658574848 |
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| author | Ning, Yu-Chien Zhou, Xin Laden, Francine Wang, Molin |
| author_facet | Ning, Yu-Chien Zhou, Xin Laden, Francine Wang, Molin |
| contents | We introduce the SoftBart approach from Bayesian ensemble learning to estimate the relationship between multipollutant mixtures and health on chronic exposures in epidemiology research. This approach offers several key advantages over existing methods: (1) it is computationally efficient and well-suited for analyzing large datasets; (2) it is flexible in estimating various correlated nonlinear functions simultaneously; and (3) it accurately identifies active variables within highly correlated multipollutant mixtures. Through simulations, we demonstrate the method's superiority by comparing its accuracy in estimating and quantifying uncertainties for both main and interaction effects with the commonly used method, BKMR. Last, we apply the method to analyze a multipollutant dataset with 10,110 participates from the Nurses' Health Study. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_17922 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bayesian ensemble learning for predicting health outcomes of multipollutant mixtures Ning, Yu-Chien Zhou, Xin Laden, Francine Wang, Molin Quantitative Methods We introduce the SoftBart approach from Bayesian ensemble learning to estimate the relationship between multipollutant mixtures and health on chronic exposures in epidemiology research. This approach offers several key advantages over existing methods: (1) it is computationally efficient and well-suited for analyzing large datasets; (2) it is flexible in estimating various correlated nonlinear functions simultaneously; and (3) it accurately identifies active variables within highly correlated multipollutant mixtures. Through simulations, we demonstrate the method's superiority by comparing its accuracy in estimating and quantifying uncertainties for both main and interaction effects with the commonly used method, BKMR. Last, we apply the method to analyze a multipollutant dataset with 10,110 participates from the Nurses' Health Study. |
| title | Bayesian ensemble learning for predicting health outcomes of multipollutant mixtures |
| topic | Quantitative Methods |
| url | https://arxiv.org/abs/2505.17922 |