title: Bayesian kernel machine regression for estimating the health effects of multi-pollutant mixtures.
abstract: Because humans are invariably exposed to complex chemical mixtures, estimating the health effects of multi-pollutant exposures is of critical concern in environmental epidemiology, and to regulatory agencies such as the U.S. Environmental Protection Agency. However, most health effects studies focus on single agents or consider simple two-way interaction models, in part because we lack the statistical methodology to more realistically capture the complexity of mixed exposures. We introduce Bayesian kernel machine regression (BKMR) as a new approach to study mixtures, in which the health outcome is regressed on a flexible function of the mixture (e.g. air pollution or toxic waste) components that is specified using a kernel function. In high-dimensional settings, a novel hierarchical variable selection approach is incorporated to identify important mixture components and account for the correlated structure of the mixture. Simulation studies demonstrate the success of BKMR in estimating the exposure-response function and in identifying the individual components of the mixture responsible for health effects. We demonstrate the features of the method through epidemiology and toxicology applications.
authors.affiliation: Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA jbobb@hsph.harvard.edu.
authors.forename: Jennifer F
authors.initials: JF
authors.lastname: Bobb
authors.affiliation: Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA.
authors.forename: Linda
authors.initials: L
authors.lastname: Valeri
authors.affiliation: Department of Environmental Health, Harvard School of Public Health, Landmark Center, 401 Park Drive, Boston, MA 02215, USA.
authors.forename: Birgit
authors.initials: B
authors.lastname: Claus Henn
authors.affiliation: Department of Environmental Health, Harvard School of Public Health, 665 Huntington Avenue, Boston, MA 02115, USA.
authors.forename: David C
authors.initials: DC
authors.lastname: Christiani
authors.affiliation: Mount Sinai Hospital, 17 East 102 Street Floor 3, West Room D3-110, New York, NY 10029, USA.
authors.forename: Robert O
authors.initials: RO
authors.lastname: Wright
authors.affiliation: Department of Environmental Health, Harvard School of Public Health, 665 Huntington Avenue, Boston, MA 02115, USA.
authors.forename: Maitreyi
authors.initials: M
authors.lastname: Mazumdar
authors.affiliation: Department of Environmental Health, Harvard School of Public Health, Landmark Center, 401 Park Drive, Boston, MA 02215, USA.
authors.forename: John J
authors.initials: JJ
authors.lastname: Godleski
authors.affiliation: Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA.
authors.forename: Brent A
authors.initials: BA
authors.lastname: Coull
mesh_terms: D000818:Animals; D001459:Bangladesh; D001499:Bayes Theorem; D056808:Biostatistics; D002675:Child, Preschool; D002658:Developmental Disabilities / Q000209:etiology; D004285:Dogs; D004782:Environmental Health / Q000706:statistics & numerical data; D004785:Environmental Pollutants / Q000009:adverse effects*; D005260:Female; D006439:Hemodynamics / Q000187:drug effects; D006801:Humans; D007223:Infant; D000069550:Machine Learning; D008670:Metals / Q000009:adverse effects; D015233:Models, Statistical; D065886:Neurodevelopmental Disorders / Q000209:etiology; D016011:Normal Distribution; D011247:Pregnancy; D012044:Regression Analysis
chemical_list: D004785:Environmental Pollutants; D008670:Metals
keywords: Air pollution; Bayesian variable selection; Environmental health; Gaussian process regression; Metal mixtures