Lisa Doove

A comparison of five recursive partitioning methods to find person subgroups involved in meaningful treatment---subgroup interactions

In case multiple treatment alternatives are available for some medical problem, the detection of treatment–subgroup interactions (i.e., relative treatment effectiveness varying over subgroups of persons) is of key importance for personalized medicine and the development of optimal treatment assignment strategies. Randomized Clinical Trials (RCT) often go without clear a priori hypotheses on the subgroups involved in treatment–subgroup interactions, and with a large number of pre-treatment characteristics in the data. In such situations, relevant subgroups (defined in terms of pre-treatment characteristics) are to be induced during the actual data analysis. This comes down to a problem of cluster analysis, with the goal of this analysis being to find clusters of persons that are involved in meaningful treatment–person cluster interactions. For such a cluster analysis, five recently proposed methods can be used, all being of a recursive partitioning type. However, these five methods have been developed almost independently, and the relations between them are not yet understood. The present paper closes this gap. It starts by outlining the basic principles behind each method, and by illustrating it with an application on an RCT data set on two treatment strategies for substance abuse problems. Next, it presents a comparison of the methods, hereby focusing on major similarities and differences. The discussion concludes with practical advice for end users with regard to the selection of a suitable method, and with an important challenge for future research in this area.

Qualitative Treatment-Subgroup Interactions in a Randomized Clinical Trial of Treatments for Adolescents with ADHD: Exploring What Cognitive-Behavioral Treatment Works for Whom

Objective This study explored qualitative treatment-subgroup interactions within data of a RCT with two cognitive behavioral treatments (CBT) for adolescents with ADHD: a planning-focused (PML) and a solution-focused CBT (SFT). Qualitative interactions imply that which treatment is best differs across subgroups of patients, and are therefore most relevant for personalized medicine. Methods Adolescents with ADHD (N = 159) received either PML or SFT. Pre-, post- and three-month follow-up data were gathered on parent-rated ADHD symptoms and planning problems. Pretreatment characteristics were explored as potential qualitative moderators of pretest to follow-up treatment effects, using an innovative analyses technique (QUINT; Dusseldorp & Van Mechelen, 2014). In addition, qualitative treatment-subgroup interactions for the therapeutic changes from pre- to posttest and from post- to follow-up test were investigated. Results For the entire time span from pretest to follow-up only a quantitative interaction was found, while from posttest to follow-up qualitative interactions were found: Adolescents with less depressive symptoms but more anxiety symptoms showed more improvement when receiving PML than SFT, while for other adolescents the effects of PML and SFT were comparable. Discussion Whereas subgroups in both treatments followed different trajectories, no subgroup was found for which SFT outperformed PML in terms of the global change in symptoms from pretest to three months after treatment. This implies that, based on this exploratory study, there is no need for personalized treatment allocation with regard to the CBTs under study for adolescents with ADHD. However, for a subgroup with comorbid anxiety symptoms but low depression PML clearly appears the treatment of preference. Trial Registration Nederlands Trial Register NTR2142

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

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 methodologically 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 interaction 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 INteraction Trees) was recently proposed to induce subgroups involved in such interactions from RCT data. 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 paper. The analysis process is described step-by-step using data from the Breast Cancer Recovery Project, showing the reader all functions included in the package. The output is explained and given a substantive interpretation. Furthermore, an overview is given of the tuning parameters involved in the analysis, along with possible motivational concerns associated with choice alternatives that are available to the user.

Deriving optimal data-analytic regimes from benchmarking studies

In benchmarking studies with simulated data sets in which two or more statistical methods are compared, over and above the search of a universally winning method, one may investigate how the winning method may vary over patterns of characteristics of the data or the data-generating mechanism. Interestingly, this problem bears strong formal similarities to the problem of looking for optimal treatment regimes in biostatistics when two or more treatment alternatives are available for the same medical problem or disease. It is outlined how optimal data-analytic regimes, that is to say, rules for optimally calling in statistical methods, can be derived from benchmarking studies with simulated data by means of supervised classification methods (e.g., classification trees). The approach is illustrated by means of analyses of data from a benchmarking study to compare two different algorithms for the estimation of a two-mode additive clustering model.

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.

A serious game for children with Attention Deficit Hyperactivity Disorder : Who benefits the most?

Objective The aim of the current study was to identify which subgroups of children with Attention Deficit Hyperactivity Disorder (ADHD) benefitted the most from playing a Serious Game (SG) intervention shown in a randomized trial to improve behavioral outcomes. Method Pre-intervention characteristics [i.e., gender, age, intellectual level of functioning, medication use, computer experience, ADHD subtype, severity of inattention problems, severity of hyperactivity/impulsivity problems, comorbid Oppositional Defiant Disorder (ODD) and Conduct Disorder (CD) symptoms] were explored as potential moderators in a Virtual Twins (VT) analysis to identify subgroups for whom the SG intervention was most effective. Primary outcome measures were parent-reported time management, planning/organizing and cooperation skills. Results Two subgroups were identified. Girls (n = 26) were identified as the subgroup that was most likely to show greater improvements in planning/organizing skills as compared to the estimated treatment effect of the total group of participants. Furthermore, among the boys, those (n = 47) with lower baseline levels of hyperactivity and higher levels of CD symptoms showed more improvements in their planning/organizing skills when they played the SG intervention as compared to the estimated treatment effect of the total group of participants. Conclusion Using a VT analysis two subgroups of children with ADHD, girls, and boys with both higher levels of CD and lower levels of hyperactivity, were identified. These subgroups mostly benefit from playing the SG intervention developed to improve ADHD related behavioral problems. Our results imply that these subgroups have a higher chance of treatment success.