ArticleBiostatistics (Oxford, England)2023
Multiple exposure distributed lag models with variable selection.
Article in Biostatistics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Multi-Tree Model for Precision Environmental Health with Longitudinally Assessed Mixture Exposure.Journal of the Royal Statistical Society. Series C, Applied statistics · 2026Article
- Modeling approaches for estimating the effects of risk factors using longitudinal lifecourse exposure data in dementia research.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Review
- Modeling Joint Health Effects of Environmental Exposure Mixtures With Bayesian Additive Regression Trees.Statistics in medicine · 2025Article
- Smooth and shape-constrained quantile distributed lag models.Biometrics · 2025Article
- 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
- LOW-RANK LONGITUDINAL FACTOR REGRESSION WITH APPLICATION TO CHEMICAL MIXTURES.The annals of applied statistics · 2025Article
- Effects of Air Quality Index and meteorological factors on cardiovascular and cerebrovascular mortality in Huizhou, China: a time-series analysis using distributed lag nonlinear models.Frontiers in public health · 2025Article
- Multiple Data Imputation Methods Advance Risk Analysis and Treatability of Co-occurring Inorganic Chemicals in Groundwater.Environmental science & technology · 2024Article
- A review of common statistical methods for dealing with multiple pollutant mixtures and multiple exposures.Frontiers in public health · 2024Review
- Revisiting the complex time-varying effect of non-pharmaceutical interventions on COVID-19 transmission in the United States.Frontiers in public health · 2024Article
- The impact of correlated exposures and missing data on multiple informant models used to identify critical exposure windows.Statistics in medicine · 2023Article
- Powering Research through Innovative Methods for Mixtures in Epidemiology (PRIME) Program: Novel and Expanded Statistical Methods.International journal of environmental research and public health · 2022Review
- Childhood exposures to environmental chemicals and neurodevelopmental outcomes in congenital heart disease.PloS one · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Distributed lag models are useful in environmental epidemiology as they allow the user to investigate critical windows of exposure, defined as the time periods during which exposure to a pollutant adversely affects health outcomes. Recent studies have focused on estimating the health effects of a large number of environmental exposures, or an environmental mixture, on health outcomes. In such settings, it is important to understand which environmental exposures affect a particular outcome, while acknowledging the possibility that different exposures have different critical windows. Further, in studies of environmental mixtures, it is important to identify interactions among exposures and to account for the fact that this interaction may occur between two exposures having different critical windows. Exposure to one exposure early in time could cause an individual to be more or less susceptible to another exposure later in time. We propose a Bayesian model to estimate the temporal effects of a large number of exposures on an outcome. We use spike-and-slab priors and semiparametric distributed lag curves to identify important exposures and exposure interactions and discuss extensions with improved power to detect harmful exposures. We then apply these methods to estimate the effects of exposure to multiple air pollutants during pregnancy on birthweight from vital records in Colorado.
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