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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.

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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.