Work

Bayesian ensemble learning method for the analysis of multipollutant mixtures data.

Year: 2025

Type: preprint

Abstract: 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.

Source: PubMed

Authors: Yu-Chien Ning, Xin Zhou, Francine Laden, Molin Wang

Institutions: Harvard University, Yale University, Brigham and Women's Hospital

Language: English

Topic: Statistical Methods in Epidemiology

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funder: National Institute of Environmental Health Sciences

Award: Statistical Methods to Account for Exposure Uncertainty in Environmental Epidemiology

OpenAlex ID: W4411078084

Cites: 1

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Related to: 10