Abstract:
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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. We develop a flexible statistical method, called lagged kernel machine regression (LKMR). To our knowledge, LKMR is the first statistical method to identify critical exposure windows of chemical mixtures, and account for complex nonlinear and non-additive effects of the mixture at any given exposure window. 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 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. We apply LKMR to estimate associations between neurodevelopment and metal mixtures in ELEMENT, a prospective birth cohort study.
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