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

IF 1.1 4区 计算机科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
R Journal Pub Date : 2025-03-01 Epub Date: 2025-08-10
Seongwon Im, Ander Wilson, Daniel Mork
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引用次数: 0

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.

估计分布滞后效应的结构化贝叶斯回归树模型:R包dlmtree。
在研究暴露与结果之间的关系时,暴露与观察到的对结果的影响之间通常存在时间滞后。分布滞后模型(DLM)是估计暴露结果与滞后测量之间关系的常用统计方法。由于重复测量通常是自相关的,滞后效应通常被限制为随时间平滑变化。关于平滑约束的最新统计发展是树形结构DLM框架。我们提出了一个R包dlmtree,可在CRAN上获得,它将树状结构的DLM和扩展集成到一个具有用户友好实现的综合软件包中。介绍了树状结构dlm的概念背景,并用模拟数据演示了每个模型的拟合过程。我们还演示了使用拟合模型的推理和解释,包括摘要和可视化。此外,它还内置了一个用于异质性分析的闪亮应用程序。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
R Journal
R Journal COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-STATISTICS & PROBABILITY
CiteScore
2.70
自引率
0.00%
发文量
40
审稿时长
>12 weeks
期刊介绍: The R Journal is the open access, refereed journal of the R project for statistical computing. It features short to medium length articles covering topics that should be of interest to users or developers of R. The R Journal intends to reach a wide audience and have a thorough review process. Papers are expected to be reasonably short, clearly written, not too technical, and of course focused on R. Authors of refereed articles should take care to: - put their contribution in context, in particular discuss related R functions or packages; - explain the motivation for their contribution; - provide code examples that are reproducible.
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