QUINT: A tool to detect qualitative treatment-subgroup interactions in randomized controlled trials

L.L. Doove, K. Van Deun, E. Dusseldorp, I. van Mechelen

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Objective:
The detection of subgroups involved in qualitative treatment–subgroup interactions (i.e., for one subgroup of clients treatment A outperforms treatment B, whereas for another the reverse holds true) is crucial for personalized health. In typical Randomized Controlled Trials (RCTs), the combination of a lack of a priori hypotheses and a large number of possible moderators leaves current methods insufficient to detect subgroups involved in such interactions. A recently developed method, QUalitative INteraction Trees (QUINT), offers a solution. However, the paper in which QUINT has been introduced is not easily accessible for non-methodologists. In this paper, we want to review the conceptual basis of QUINT in a nontechnical way, and illustrate its relevance for psychological applications.
Method:
We present a concise introduction into QUINT along with a summary of available evidence on its performance. Subsequently, we subject RCT data on the effect of motivational interviewing in a treatment for substance abuse disorders to a reanalysis with QUINT. As outcome variables, we focus on measures of retention and substance use.
Results: A qualitative treatment–subgroup interaction is found for retention. By contrast, no qualitative interaction is detected for substance use.
Conclusions:
QUINT may lead to insightful and well-interpretable results with straightforward implications for personalized treatment assignment.
Original languageEnglish
Pages (from-to)612-622
JournalPsychotherapy Research
Volume26
Issue number5
DOIs
Publication statusPublished - 2016

Cite this

Doove, L.L. ; Van Deun, K. ; Dusseldorp, E. ; van Mechelen, I. / QUINT : A tool to detect qualitative treatment-subgroup interactions in randomized controlled trials. In: Psychotherapy Research. 2016 ; Vol. 26, No. 5. pp. 612-622.
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title = "QUINT: A tool to detect qualitative treatment-subgroup interactions in randomized controlled trials",
abstract = "Objective: The detection of subgroups involved in qualitative treatment–subgroup interactions (i.e., for one subgroup of clients treatment A outperforms treatment B, whereas for another the reverse holds true) is crucial for personalized health. In typical Randomized Controlled Trials (RCTs), the combination of a lack of a priori hypotheses and a large number of possible moderators leaves current methods insufficient to detect subgroups involved in such interactions. A recently developed method, QUalitative INteraction Trees (QUINT), offers a solution. However, the paper in which QUINT has been introduced is not easily accessible for non-methodologists. In this paper, we want to review the conceptual basis of QUINT in a nontechnical way, and illustrate its relevance for psychological applications. Method: We present a concise introduction into QUINT along with a summary of available evidence on its performance. Subsequently, we subject RCT data on the effect of motivational interviewing in a treatment for substance abuse disorders to a reanalysis with QUINT. As outcome variables, we focus on measures of retention and substance use. Results: A qualitative treatment–subgroup interaction is found for retention. By contrast, no qualitative interaction is detected for substance use. Conclusions: QUINT may lead to insightful and well-interpretable results with straightforward implications for personalized treatment assignment.",
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QUINT : A tool to detect qualitative treatment-subgroup interactions in randomized controlled trials. / Doove, L.L.; Van Deun, K.; Dusseldorp, E.; van Mechelen, I.

In: Psychotherapy Research, Vol. 26, No. 5, 2016, p. 612-622.

Research output: Contribution to journalArticleScientificpeer-review

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AB - Objective: The detection of subgroups involved in qualitative treatment–subgroup interactions (i.e., for one subgroup of clients treatment A outperforms treatment B, whereas for another the reverse holds true) is crucial for personalized health. In typical Randomized Controlled Trials (RCTs), the combination of a lack of a priori hypotheses and a large number of possible moderators leaves current methods insufficient to detect subgroups involved in such interactions. A recently developed method, QUalitative INteraction Trees (QUINT), offers a solution. However, the paper in which QUINT has been introduced is not easily accessible for non-methodologists. In this paper, we want to review the conceptual basis of QUINT in a nontechnical way, and illustrate its relevance for psychological applications. Method: We present a concise introduction into QUINT along with a summary of available evidence on its performance. Subsequently, we subject RCT data on the effect of motivational interviewing in a treatment for substance abuse disorders to a reanalysis with QUINT. As outcome variables, we focus on measures of retention and substance use. Results: A qualitative treatment–subgroup interaction is found for retention. By contrast, no qualitative interaction is detected for substance use. Conclusions: QUINT may lead to insightful and well-interpretable results with straightforward implications for personalized treatment assignment.

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