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Related Experiment Video

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Detecting treatment-subgroup interactions in clustered data with generalized linear mixed-effects model trees.

M Fokkema1, N Smits2, A Zeileis3

  • 1, Wassenaarseweg 52, 2333, AK, Leiden, Netherlands. m.fokkema@fsw.leidenuniv.nl.

Behavior Research Methods
|October 27, 2017
PubMed
Summary

A new algorithm, the generalized linear mixed-effects model tree (GLMM tree), identifies patient subgroups benefiting from specific treatments. This method accurately detects treatment-subgroup interactions in clustered data, advancing personalized medicine.

Keywords:
Classification and regression treesMixed-effects modelsModel-based recursive partitioningTreatment-subgroup interactions

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Area of Science:

  • Statistics
  • Machine Learning
  • Psychological Research

Background:

  • Personalized medicine requires identifying patient subgroups for differential treatment effectiveness.
  • Existing tree-based algorithms struggle with clustered or nested data structures common in psychological research.

Purpose of the Study:

  • To introduce the generalized linear mixed-effects model tree (GLMM tree) algorithm.
  • To enable the detection of treatment-subgroup interactions in clustered datasets.

Main Methods:

  • The GLMM tree algorithm utilizes model-based recursive partitioning.
  • It incorporates generalized linear mixed-effects models (GLMMs) to estimate random-effects parameters.
  • The method is designed to handle clustered data structures.

Main Results:

  • GLMM trees demonstrated superior accuracy in recovering treatment-subgroup interactions compared to existing methods.
  • The algorithm showed higher predictive accuracy and lower type II error rates in simulations.
  • GLMM trees offered comparable or slightly better predictive accuracy than pre-specified interaction models.

Conclusions:

  • GLMM trees are a valuable exploratory tool for uncovering treatment-subgroup interactions.
  • The algorithm effectively addresses the challenge of clustered data in analyzing treatment effects.
  • This approach holds promise for advancing personalized medicine, particularly in psychological research.