ArticleBiostatistics (Oxford, England)2022
Treed distributed lag nonlinear models.
Article in Biostatistics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 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
- Duration of agricultural pesticide exposure application and Parkinson's disease in California's central valley.Environmental research, health : ERH · 2026Article
- Associations of temperature, particulate matter, and greenness with human papillomavirus positivity at screening: an analysis of Guangdong Province (2016-2022) using the treed distributed lag nonlinear model.Frontiers in public health · 2026Article
- Time-series analysis of vitiligo-related online search behavior in response to ambient air pollutants.Scientific reports · 2025Article
- Broad Applications of Distributed Lag Non-Linear Model in Public Health: A Comprehensive Review.GeoHealth · 2025Review
- A novel approach for inferring effects on pregnancy loss.American journal of epidemiology · 2025Article
- Critical window of gestational greenspace exposure for the risk of low birth weight.Environmental research letters : ERL [Web site] · 2025Article
- Quantifying effects of solar power adoption on COScience advances · 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
- Structured Bayesian Regression Tree Models for Estimating Distributed Lag Effects: The R Package dlmtree.The R journal · 2025Article
- Association Between Diurnal Temperature Range and Risk of Cardiomyopathy-Induced Hospitalisation in Henan, China: A Time-Series Study.Risk management and healthcare policy · 2025Article
- Heterogeneous Distributed Lag Models to Estimate Personalized Effects of Maternal Exposures to Air Pollution.Journal of the American Statistical Association · 2024Article
- A review of common statistical methods for dealing with multiple pollutant mixtures and multiple exposures.Frontiers in public health · 2024Review
- 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
- Bias Amplification and Variance Inflation in Distributed Lag Models Using Low-Spatial-Resolution Data.American journal of epidemiology · 2023Article
- Estimating perinatal critical windows of susceptibility to environmental mixtures via structured Bayesian regression tree pairs.Biometrics · 2023Article
- Time-lagged relationships between a decade of air pollution exposure and first hospitalization with Alzheimer's disease and related dementias.Environment international · 2023Article
- KERNEL MACHINE AND DISTRIBUTED LAG MODELS FOR ASSESSING WINDOWS OF SUSCEPTIBILITY TO ENVIRONMENTAL MIXTURES IN CHILDREN'S HEALTH STUDIES.The annals of applied statistics · 2022Article
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2 authors.
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Abstract
In studies of maternal exposure to air pollution, a children's health outcome is regressed on exposures observed during pregnancy. The distributed lag nonlinear model (DLNM) is a statistical method commonly implemented to estimate an exposure-time-response function when it is postulated the exposure effect is nonlinear. Previous implementations of the DLNM estimate an exposure-time-response surface parameterized with a bivariate basis expansion. However, basis functions such as splines assume smoothness across the entire exposure-time-response surface, which may be unrealistic in settings where the exposure is associated with the outcome only in a specific time window. We propose a framework for estimating the DLNM based on Bayesian additive regression trees. Our method operates using a set of regression trees that each assume piecewise constant relationships across the exposure-time space. In a simulation, we show that our model outperforms spline-based models when the exposure-time surface is not smooth, while both methods perform similarly in settings where the true surface is smooth. Importantly, the proposed approach is lower variance and more precisely identifies critical windows during which exposure is associated with a future health outcome. We apply our method to estimate the association between maternal exposures to PM$_{2.5}$ and birth weight in a Colorado, USA birth cohort.
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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.