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Structured Bayesian Regression Tree Models for Estimating Distributed Lag Effects: The R Package dlmtree
Seongwon Im1, Ander Wilson1, Daniel Mork2
1Department of Statistics, Colorado State University, United States of America.
This study introduces dlmtree, an R package for tree-structured distributed lag models (DLMs). It simplifies analyzing exposure-outcome relationships with time lags and smooth effects, aiding researchers in complex statistical modeling.
Area of Science:
- Statistics
- Biostatistics
- Computational Statistics
Background:
- Exposure-outcome relationships often involve time lags.
- Distributed Lag Models (DLMs) estimate these lagged effects.
- Autocorrelated data requires smooth constraints on lagged effects.
Purpose of the Study:
- Introduce the R package dlmtree for tree-structured DLMs.
- Provide a user-friendly implementation of advanced DLM techniques.
- Facilitate the analysis of exposure-outcome relationships with time lags.
Main Methods:
- Utilizes a tree-structured distributed lag model (DLM) framework.
- Integrates extensions for comprehensive statistical modeling.
- Provides user-friendly implementation in an R package.
Main Results:
- The dlmtree package offers seamless integration of tree-structured DLMs.
- Demonstrates fitting, inference, and interpretation with simulated data.
- Includes a Shiny app for heterogeneity analysis.
Conclusions:
- dlmtree provides a comprehensive and accessible tool for researchers.
- Enables robust analysis of time-lagged exposure-outcome associations.
- Facilitates advanced statistical modeling and data visualization.
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