Lisa L Doove1, Katrijn Van Deun1,2, Elise Dusseldorp1,3, Iven Van Mechelen1. 1. a Department of Psychology and Educational Sciences , Katholieke Universiteit Leuven , Leuven , Belgium. 2. b Department of Methodology and Statistics , Tilburg University , Tilburg , The Netherlands. 3. c Mathematical Institute, Leiden University , Leiden , The Netherlands.
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.
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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