ArticleBiostatistics (Oxford, England)2018
Lagged kernel machine regression for identifying time windows of susceptibility to exposures of complex mixtures.
Article in Biostatistics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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Who cites it
45 citing papers in PubMed, 59 citations in OpenAlex.
- Multi-Tree Model for Precision Environmental Health with Longitudinally Assessed Mixture Exposure.Journal of the Royal Statistical Society. Series C, Applied statistics · 2026Article
- Time-Varying Exposure to Element Mixtures and Children's Cognition at 5 Years of Age: Findings from the New Hampshire Birth Cohort Study.Environmental health perspectives · 2026Article
- Statistical Methods for Chemical Mixtures: A Roadmap for Practitioners Using Simulation Studies and a Sample Data Analysis in the PROTECT Cohort.Environmental health perspectives · 2025Article
- Dynamic Single-Index Scalar-On-Function Model.Statistics in medicine · 2025Article
- Neurobehavioral effects of low dose exposure to chemical mixtures: a review.Archives of toxicology · 2025Review
- Periods of susceptibility for associations between phthalate exposure and preterm birth: Results from a pooled analysis of 16 US cohorts.Environment international · 2025Article
- LOW-RANK LONGITUDINAL FACTOR REGRESSION WITH APPLICATION TO CHEMICAL MIXTURES.The annals of applied statistics · 2025Article
- Exposome Burden Scores to Summarize Environmental Chemical Mixtures: Creating a Fair and Common Scale for Cross-study Harmonization, Report-back and Precision Environmental Health.Current environmental health reports · 2025Review
- Endocrine Disruptors in Child Obesity and Related Disorders: Early Critical Windows of Exposure.Current nutrition reports · 2025Review
- Distributed lag models for retrospective cohort data with application to a study of built environment and body weight.Biometrics · 2025Article
- Detecting univariate, bivariate, and overall effects of drug mixtures using Bayesian kernel machine regression.The American journal of drug and alcohol abuse · 2024Article
- Applying Latent Variable Models to Estimate Cumulative Exposure Burden to Chemical Mixtures and Identify Latent Exposure Subgroups: A Critical Review and Future Directions.Statistics in biosciences · 2024Article
- Modeling Historic Arsenic Exposures and Spatial Risk for Bladder Cancer.Statistics in biosciences · 2024Article
- Investigating the Interplay of Toxic Metals and Essential Elements in Liver Disease.International journal of environmental research and public health · 2024Article
- Identifying critical windows of prenatal phenol, paraben, and pesticide exposure and child neurodevelopment: Findings from a prospective cohort study.The Science of the total environment · 2024Article
- Association of combined lead, cadmium, and mercury with systemic inflammation.Frontiers in public health · 2024Article
- Prenatal Metal Exposures and Child Social Responsiveness Scale Scores in 2 Prospective Studies.Environmental health insights · 2024Article
- Multiple exposure distributed lag models with variable selection.Biostatistics (Oxford, England) · 2023Article
- Semiparametric distributed lag quantile regression for modeling time-dependent exposure mixtures.Biometrics · 2023Article
- Chemical Safety and the Exposome.Emerging contaminants · 2023Article
Corrections and comments
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Authors and funding
13 authors at 7 institutions in 4 countries.
Funding
Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.