BiMM树：一种用于对聚类和纵向二元结局进行建模的决策树方法。
BiMM tree: A decision tree method for modeling clustered and longitudinal binary outcomes.
作者信息
Speiser Jaime Lynn, Wolf Bethany J, Chung Dongjun, Karvellas Constantine J, Koch David G, Durkalski Valerie L
机构信息
Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC.
Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC.
出版信息
Commun Stat Simul Comput. 2020;49(4):1004-1023. doi: 10.1080/03610918.2018.1490429. Epub 2018 Sep 12.
Abstract
Clustered binary outcomes are frequently encountered in clinical research (e.g. longitudinal studies). Generalized linear mixed models (GLMMs) for clustered endpoints have challenges for some scenarios (e.g. data with multi-way interactions and nonlinear predictors unknown ). We develop an alternative, data-driven method called Binary Mixed Model (BiMM) tree, which combines decision tree and GLMM within a unified framework. Simulation studies show that BiMM tree achieves slightly higher or similar accuracy compared to standard methods. The method is applied to a real dataset from the Acute Liver Failure Study Group.
摘要
聚类二元结局在临床研究（如纵向研究）中经常遇到。用于聚类终点的广义线性混合模型（GLMM）在某些情况下存在挑战（如具有多向交互作用和未知非线性预测变量的数据）。我们开发了一种替代的、数据驱动的方法，称为二元混合模型（BiMM）树，它在一个统一的框架内结合了决策树和GLMM。模拟研究表明，与标准方法相比，BiMM树的准确率略高或相近。该方法应用于急性肝衰竭研究组的一个真实数据集。
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