Related Experiment Video
Updated: Dec 22, 2025

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
Published on: September 17, 2019
BiMM tree: A decision tree method for modeling clustered and longitudinal binary outcomes
Jaime Lynn Speiser1, Bethany J Wolf2, Dongjun Chung2
1Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC.
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 a priori). 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.
Related Concept Videos
Survival Tree
Building a Survival Tree
Constructing a...
Comparing the Survival Analysis of Two or More Groups
Contingency Table
Truncation in Survival Analysis
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
Assumptions of Survival Analysis
Statistical Methods for Analyzing Epidemiological Data

