Open AccessModel choice for estimating the association between exposure to chemical mixtures and health outcomes: A simulation study
Author(s) -
Lauren Hoskovec,
Wande Benka-Coker,
Rachel Severson,
Sheryl Magzamen,
Ander Wilson
Publication year - 2021
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0249236
Subject(s) - bayes' theorem , computer science , bayesian probability , nonparametric statistics , lung function , machine learning , association (psychology) , statistics , data mining , artificial intelligence , mathematics , medicine , psychology , lung , psychotherapist
Challenges arise in researching health effects associated with chemical mixtures. Several methods have recently been proposed for estimating the association between health outcomes and exposure to chemical mixtures, but a formal simulation study comparing broad-ranging methods is lacking. We select five recently developed methods and evaluate their performance in estimating the exposure-response function, identifying active mixture components, and identifying interactions in a simulation study. Bayesian kernel machine regression (BKMR) and nonparametric Bayes shrinkage (NPB) were top-performing methods in our simulation study. BKMR and NPB outperformed other contemporary methods and traditional linear models in estimating the exposure-response function and identifying active mixture components. BKMR and NPB produced similar results in a data analysis of the effects of multipollutant exposure on lung function in children with asthma.