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.