Abstract
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 datasets.
Highlights
In research on the efficacy of treatments for somatic and psychological disorders, the one-size-fits-all paradigm is slowly losing ground, and personalized or stratified medicine is becoming increasingly important
We focus on trees where the terminal nodes consist of a linear (LM) or generalized linear model (GLM), in which case the predicted value for a new observation is determined by the node-specific parameter estimates of the (G)Linear model. linear mixed-effects models (LMMs) (LM), while adjusting for random effects
The remainder of this paper is structured into four sections: In the first section, we introduce the Generalized linear mixed model. IPDMA (GLMM) tree algorithm using an artificial motivating dataset with treatmentsubgroup interactions
Summary
In research on the efficacy of treatments for somatic and psychological disorders, the one-size-fits-all paradigm is slowly losing ground, and personalized or stratified medicine is becoming increasingly important. Stratified medicine presents the challenge of discovering which patients respond best to which treatments This can be referred to as the detection of treatment-subgroup interactions (e.g., Doove, Dusseldorp, Van Deun, & Van Mechelen, 2014). Treatment-subgroup interactions are studied using linear models, such as factorial analysis of variance techniques, in which potential moderators have to be specified a priori, have to be checked one at a time, and continuous moderator variables have to be discretized. This may hamper identification of which treatment works best for whom, especially when there are no a priori hypotheses about treatment-subgroup interactions. As noted by Kraemer, Frank, and Kupfer (2006), there is a need for methods that generate instead of test such hypotheses
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