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Biostatistics. 2018 Jul 1;19(3):325-341. doi: 10.1093/biostatistics/kxx036.

Lagged kernel machine regression for identifying time windows of susceptibility to exposures of complex mixtures.

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Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, New York, NY 10029, USA.
Biostatistics Unit, Kaiser Permanente Washington Health Research Institute, 1730 Minor Avenue, Seattle, WA 98101, USA.
Epidemiology and Biostatistics Core, The Forsyth Institute, 245 First Street, Cambridge, MA 02142, USA.
Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, 17 E. 102nd Street, New York, NY 10029, USA.
Department of Environmental Health, Boston University School of Public Health, 715 Albany Street, Boston, MA 02118, USA.
Department of Environmental Health, Harvard T.H. Chan School of Public Health, 665 Huntington Avenue, Boston, MA 02115, USA.
Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, 17 East 102nd Street, New York, NY 10029, USA.
Division of Research in Community Interventions, National Institute of Perinatology, Montes Urales 800, Lomas Virreyes, CP 11000, CDMX, México.
Center for Nutrition and Health Research, National Institute of Public Health, Universidad No. 655 Colonia Santa María, Ahuacatitlán, Cerrada Los Pinos y Caminera, C.P. 62100, Cuernavaca, Morelos, México.
Dalla Lana School of Public Health, University of Toronto, 155 College Street, Toronto, ON, M5T 3M7, Canada.
Department of Biostatistics, Harvard T.H. Chan School of Public Health, 665 Huntington Avenue, Boston, MA 02115, USA.


The impact of neurotoxic chemical mixtures on children's health is a critical public health concern. It is well known that during early life, toxic exposures may impact cognitive function during critical time intervals of increased vulnerability, known as windows of susceptibility. Knowledge on time windows of susceptibility can help inform treatment and prevention strategies, as chemical mixtures may affect a developmental process that is operating at a specific life phase. There are several statistical challenges in estimating the health effects of time-varying exposures to multi-pollutant mixtures, such as: multi-collinearity among the exposures both within time points and across time points, and complex exposure-response relationships. To address these concerns, we develop a flexible statistical method, called lagged kernel machine regression (LKMR). LKMR identifies critical exposure windows of chemical mixtures, and accounts for complex non-linear and non-additive effects of the mixture at any given exposure window. Specifically, LKMR estimates how the effects of a mixture of exposures change with the exposure time window using a Bayesian formulation of a grouped, fused lasso penalty within a kernel machine regression (KMR) framework. A simulation study demonstrates the performance of LKMR under realistic exposure-response scenarios, and demonstrates large gains over approaches that consider each time window separately, particularly when serial correlation among the time-varying exposures is high. Furthermore, LKMR demonstrates gains over another approach that inputs all time-specific chemical concentrations together into a single KMR. We apply LKMR to estimate associations between neurodevelopment and metal mixtures in Early Life Exposures in Mexico and Neurotoxicology, a prospective cohort study of child health in Mexico City.

[Available on 2019-07-01]
[Indexed for MEDLINE]

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