Work

Structured Bayesian Regression Tree Models for Estimating Distributed Lag Effects: The R Package dlmtree

Year: 2025

Type: article

Abstract: When examining the relationship between an exposure and an outcome, there is often a time lag between exposure and the observed effect on the outcome. A common statistical approach for estimating the relationship between the outcome and lagged measurements of exposure is a distributed lag model (DLM). Because repeated measurements are often autocorrelated, the lagged effects are typically constrained to vary smoothly over time. A recent statistical development on the smoothing constraint is a tree structured DLM framework. We present an R package dlmtree, available on CRAN, that integrates tree structured DLM and extensions into a comprehensive software package with user-friendly implementation. A conceptual background on tree structured DLMs and demonstration of the fitting process of each model using simulated data are provided. We also demonstrate inference and interpretation using the fitted models, including summary and visualization. Additionally, a built-in shiny app for heterogeneity analysis is included.

Source: The R Journal

DOI: 10.32614/rj-2025-007

Authors: Seongwon Im, Ander Wilson, Daniel Mork

Institutions: Colorado State University, Harvard University

Language: English

Topic: Data Analysis with R

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funders: National Institutes of Health, National Institute on Aging, National Institute of Environmental Health Sciences

Awards: Air Pollution and Pregnancy Loss, Methods for Data Integration and Risk Assessment for Environmental Mixtures, Statistical methods to characterize causal mechanisms by which air pollution affects the recurrence of cardiovascular events, Statistical Methods for Precision Environmental Health with Mixture Exposures, National Cohort Studies of Alzheimer's Disease, Related Dementias and Air Pollution