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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Published on: July 3, 2020

Tailoring Bayesian Additive Regression Trees (BART) for environmental mixture studies.

Kaizong Ye1, Zhen Chen2, Shanshan Zhao1

  • 1Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Durham, North Carolina, United States of America.

Plos One
|May 11, 2026
PubMed
Summary
This summary is machine-generated.

A modified Bayesian Additive Regression Trees (BART) model effectively analyzes environmental mixtures, outperforming BKMR in prediction and identifying key chemicals like POPs impacting health outcomes.

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Area of Science:

  • Environmental Health
  • Statistical Modeling
  • Toxicology

Background:

  • Investigating complex effects of environmental mixtures on human health is challenging.
  • Tree ensemble methods like BART offer stability and accuracy for high-dimensional data.
  • BART's application in environmental mixtures analysis requires further study.

Purpose of the Study:

  • Tailor BART for environmental mixtures to identify toxic agents and predict health outcomes.
  • Incorporate covariate adjustment and hierarchical variable selection for grouped chemicals.
  • Compare modified BART performance against Bayesian Kernel Machine Regression (BKMR).

Main Methods:

  • Developed a modified BART model with smooth response surfaces and covariate adjustment.
  • Implemented component-wise and hierarchical variable selection for chemical groupings.
  • Utilized Generalized Additive Model (GAM) approximation for interpreting individual chemical contributions.
  • Evaluated performance via simulations and a National Health and Nutrition Examination Survey (NHANES) case study on persistent organic pollutants (POPs) and telomere length.

Main Results:

  • Modified BART showed comparable or superior prediction accuracy ([Formula: see text] > 0.7) to BKMR in simulations.
  • Hierarchical variable selection with modified BART yielded higher [Formula: see text] (0.82-0.99) than BKMR (0.59-0.67) for grouped chemicals.
  • Modified BART reduced computational time by 70-99.8% compared to BKMR.
  • Identified POPs (2,3,4,7,8-pncdf, PCB126, PCB169) with positive effects on telomere length in the NHANES data.

Conclusions:

  • Modified BART is a robust, scalable alternative to BKMR for environmental mixtures analysis.
  • It excels with large datasets, binary outcomes, and grouped chemicals.
  • GAM approximation aids interpretation of individual chemical effects from complex models.