Title: Quint: An R package for identification of subgroups of clients who differ 
in which treatment alternative is best for them

Author: Elise Dusseldorp, Lisa Doove, Iven van Mechelen

Affiliation: Netherlands Organisation for Applied Scientific Research

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 methodological
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
interactions 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 INter- action Trees) was recently proposed to induce subgroups
involved in such interactions from RCT data (Dusseldorp & Van Mechelen,
2014).  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 presentation.  The
analysis process is described step-by-step using a real data example,
showing the listener the main functions included in the package.  The
output is explained and given a substantive interpretation.  Furthermore,
an overview is given of the tuning parame- ters involved in the analysis,
along with possible motivational concerns associated with choice
alternatives that are available to the user.

Keywords: treatment-subgroup interaction, treatment efficacy, subgroup
analysis, re- gression trees, R package

References

Dusseldorp, E., & Van Mechelen, I. (2014).  Qualitative interaction trees:
A tool to identify qualitative treatment-subgroup interactions.  Statistics
in Medicine, 33 , 219-237.