Title: Qualitative interaction trees: a tool to identify qualitative treatment-subgroup interactions
Authors: Dusseldorp, Elise ×
Van Mechelen, Iven #
Issue Date: 2014
Publisher: John Wiley & Sons
Series Title: Statistics in Medicine vol:33 issue:2 pages:219-237
Abstract: When two alternative treatments (A and B) are available, some subgroup of patients may display a better
outcome with treatment A than with B, whereas for another subgroup, the reverse may be true. If this is the
case, a qualitative (i.e., disordinal) treatment–subgroup interaction is present. Such interactions imply that some
subgroups of patients should be treated differently and are therefore most relevant for personalized medicine. In
case of data from randomized clinical trials with many patient characteristics that could interact with treatment
in a complex way, a suitable statistical approach to detect qualitative treatment–subgroup interactions is not
yet available. As a way out, in the present paper, we propose a new method for this purpose, called QUalitative
INteraction Trees (QUINT). QUINT results in a binary tree that subdivides the patients into terminal nodes on
the basis of patient characteristics; these nodes are further assigned to one of three classes: a first for which A is
better than B, a second for which B is better than A, and an optional third for which type of treatment makes no
difference. Results of QUINT on simulated data showed satisfactory performance, with regard to optimization
and recovery. Results of an application to real data suggested that, compared with other approaches, QUINT
provided a more pronounced picture of the qualitative interactions that are present in the data.
ISSN: 0277-6715
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Quantitative Psychology and Individual Differences
× corresponding author
# (joint) last author

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