title: Quint: An R package for the identification of subgroups of clients who differ in which treatment alternative is best for them.
abstract: In the analysis of randomized controlled trials (RCTs), treatment effect heterogeneity often occurs, implying differences across (subgroups of) clients in treatment efficacy. This phenomenon is typically referred to as treatment-subgroup interactions. The identification of subgroups of clients, defined in terms of pretreatment characteristics that are involved in a treatment-subgroup interaction, is a methodologically challenging task, especially when many characteristics are available that may interact with treatment and when no comprehensive a priori hypotheses on relevant subgroups are available. A special type of treatment-subgroup interaction occurs if the ranking of treatment alternatives in terms of efficacy differs across subgroups of clients (e.g., for one subgroup treatment A is better than B and for another subgroup treatment B is better than A). These are called qualitative treatment-subgroup interactions and are most important for optimal treatment assignment. The method QUINT (Qualitative INteraction Trees) was recently proposed to induce subgroups involved in such interactions from RCT data. The result of an analysis with QUINT is a binary tree from which treatment assignment criteria can be derived. The implementation of this method, the R package quint, is the topic of this paper. The analysis process is described step-by-step using data from the Breast Cancer Recovery Project, showing the reader all functions included in the package. The output is explained and given a substantive interpretation. Furthermore, an overview is given of the tuning parameters involved in the analysis, along with possible motivational concerns associated with choice alternatives that are available to the user.
authors.affiliation: Department of Psychology, Katholieke Universiteit Leuven, Leuven, Belgium. elise.dusseldorp@math.leidenuniv.nl.|Mathematical Institute, Leiden University, PO Box 9512, 2300, RA, Leiden, The Netherlands. elise.dusseldorp@math.leidenuniv.nl.|Netherlands Organization for Applied Scientific Research (TNO), Leiden, The Netherlands. elise.dusseldorp@math.leidenuniv.nl.
authors.forename: Elise
authors.initials: E
authors.lastname: Dusseldorp
authors.affiliation: Department of Psychology, Katholieke Universiteit Leuven, Leuven, Belgium.
authors.forename: Lisa
authors.initials: L
authors.lastname: Doove
authors.affiliation: Department of Psychology, Katholieke Universiteit Leuven, Leuven, Belgium.
authors.forename: Iven van
authors.initials: Iv
authors.lastname: Mechelen
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000628:therapy; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*; D020127:Recovery of Function; D012107:Research Design; D012984:Software; D016896:Treatment Outcome
keywords: Computer software; Moderator; Regression trees; Subgroup analysis; Treatment efficacy; Treatment-subgroup interaction