Model choice for estimating the association between exposure to chemical mixtures and health outcomes: A simulation study.
Journal
PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081
Informations de publication
Date de publication:
2021
2021
Historique:
received:
08
10
2020
accepted:
13
03
2021
entrez:
25
3
2021
pubmed:
26
3
2021
medline:
21
10
2021
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 33765068
doi: 10.1371/journal.pone.0249236
pii: PONE-D-20-31686
pmc: PMC7993848
doi:
Substances chimiques
Air Pollutants
0
Particulate Matter
0
Pesticides
0
Ozone
66H7ZZK23N
Nitrogen Dioxide
S7G510RUBH
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
e0249236Subventions
Organisme : NIEHS NIH HHS
ID : K22 ES023815
Pays : United States
Organisme : NIEHS NIH HHS
ID : R01 ES028811
Pays : United States
Déclaration de conflit d'intérêts
The authors have declared that no competing interests exist.
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