Generalized linear mixed-effects model trees, especially for detecting treatment-subgroup interactions in clustered data. Accompanied by the R package glmertree, combining partykit::glmtree and lme4::glmer.
Marjolein Fokkema, Niels Smits, Achim Zeileis, Torsten Hothorn, Henk Kelderman (2018).
“Detecting Treatment-Subgroup Interactions in Clustered Data with Generalized Linear Mixed-Effects Model Trees.”
Behavior Research Methods. Forthcoming.
Identification of subgroups of patients for whom
treatment A is more effective than treatment B, and vice
versa, is of key importance to the development of
personalized medicine. Tree-based algorithms are helpful tools for
the detection of such interactions, but none of the available
algorithms allow for taking into account clustered or nested
dataset structures, which are particularly common in
psychological research. Therefore, we propose the generalized
linear mixed-effects model tree (GLMM tree) algorithm,
which allows for the detection of treatment-subgroup
interactions, while accounting for the clustered structure of a
dataset. The algorithm uses model-based recursive
partitioning to detect treatment-subgroup interactions, and a GLMM
to estimate the random-effects parameters. In a simulation
study, GLMM trees show higher accuracy in recovering
treatment-subgroup interactions, higher predictive accuracy,
and lower type II error rates than linear-model-based recursive
partitioning and mixed-effects regression trees. Also,
GLMM trees show somewhat higher predictive accuracy
than linear mixed-effects models with pre-specified interaction
effects, on average. We illustrate the application
of GLMM trees on an individual patient-level data meta-analysis
on treatments for depression. We conclude that
GLMM trees are a promising exploratory tool for the
detection of treatment-subgroup interactions in clustered
GLMM tree for treatment-subgroup interaction in a motivating artificial dataset.