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基于广义线性混合模型 (GLMM) 树的线性增长曲线模型中的子群检测。

Subgroup detection in linear growth curve models with generalized linear mixed model (GLMM) trees.

机构信息

Unit of Methodology and Statistics, Institute of Psychology, Leiden University, Leiden, The Netherlands.

Department of Statistics, Faculty of Economics and Statistics, Universität Innsbruck, Innsbruck, Austria.

出版信息

Behav Res Methods. 2024 Oct;56(7):6759-6780. doi: 10.3758/s13428-024-02389-1. Epub 2024 May 29.

DOI:10.3758/s13428-024-02389-1
PMID:38811518
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11543751/
Abstract

Growth curve models are popular tools for studying the development of a response variable within subjects over time. Heterogeneity between subjects is common in such models, and researchers are typically interested in explaining or predicting this heterogeneity. We show how generalized linear mixed-effects model (GLMM) trees can be used to identify subgroups with different trajectories in linear growth curve models. Originally developed for clustered cross-sectional data, GLMM trees are extended here to longitudinal data. The resulting extended GLMM trees are directly applicable to growth curve models as an important special case. In simulated and real-world data, we assess performance of the extensions and compare against other partitioning methods for growth curve models. Extended GLMM trees perform more accurately than the original algorithm and LongCART, and similarly accurate compared to structural equation model (SEM) trees. In addition, GLMM trees allow for modeling both discrete and continuous time series, are less sensitive to (mis-)specification of the random-effects structure and are much faster to compute.

摘要

生长曲线模型是研究随时间变化的个体内响应变量发展的常用工具。此类模型中，个体之间的异质性很常见，研究人员通常有兴趣解释或预测这种异质性。我们展示了广义线性混合效应模型（GLMM）树如何用于识别线性生长曲线模型中具有不同轨迹的亚组。GLMM 树最初是为聚类横截面数据开发的，这里将其扩展到纵向数据。由此产生的扩展 GLMM 树可作为一个重要特例直接应用于生长曲线模型。在模拟和真实世界的数据中，我们评估了扩展的性能，并与生长曲线模型的其他分区方法进行了比较。扩展的 GLMM 树比原始算法和 LongCART 具有更高的准确性，与结构方程模型（SEM）树的准确性相似。此外，GLMM 树允许对离散和连续时间序列进行建模，对随机效应结构的（错误）指定不那么敏感，并且计算速度更快。

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