A multilevel machine learning algorithm to predict session-by-session outcome for patients receiving cognitive-behavioural therapy

Behaviour Research and Therapy, vol. 193, pp. 104848

Abstract

Aims New innovations in predictive models, such as machine learning, could enhance the effectiveness of measurement-based care systems by generating more accurate session-by-session psychotherapy outcome predictions. In this study, we developed a tree-based model that integrates the strengths of multilevel and machine learning models to predict patients’ trajectories of clinical improvement during cognitive-behavioural therapy (CBT). Methods We used a sample of 1,008 outpatients who were treated at a CBT university clinic in Germany. The total sample was randomly divided into a training (2/3 of the sample) and a test (remaining 1/3) set. Grounded on patient demographic and clinical information at baseline, we developed a generalized linear mixed model tree algorithm to predict patients’ session-by-session outcome change during the first ten sessions. Results : The best-fitting model in the training set identified 10 groups of patients based on their presenting characteristics and improvement trajectories. In the test set, the algorithm resulted in a correlation of .65 between the observed and predicted values for the outcome variable (cross-validation R 2 = .42). Developing failure boundaries based on the tree-based approach allowed us to correctly identify 67.6% of the test set patients who did not reliably improve within the first 15 sessions of treatment. Discussion : This study provides preliminary support for the integration of multilevel and machine learning models via generalized linear mixed model trees. The algorithms developed could help support routine implementation of precision mental health care strategies by informing therapists’ treatment planning and session-by-session responsiveness for different patient subgroups.

Authors 7

1. Juan Martín Gómez Penedo corresponding

   University of Kassel · Osnabrück University

   Affiliation as printed

   Clinical Psychology and Psychotherapy Department, Universität Osnabrück, Lise-Meitner-Str. 3, 49076, Osnabrück, Germany; Clinical Psychology and Psychotherapy Department, Universität Kassel, Kassel, Germany. Electronic address: martin.gomezpenedo@uni-osnabrueck.de

2. Alice E. Coyne

   American University

   Affiliation as printed

   Department of Psychology, College of Arts and Sciences, American University, Asbury Building, Asbury 314, Washington DC, USA

3. Manuel Meglio

   Consejo Nacional de Investigaciones Científicas y Técnicas · Universidad de Buenos Aires

   Affiliation as printed

   Department of Psychology, University of Buenos Aires (CONICET), Buenos Aires, Argentina

4. Marjolein Fokkema Aachen Methodology and Statistics in Psychology Department

   Leiden University

   Affiliation as printed

   Methodology and Statistics in Psychology Department, Leiden University, Leiden, the Netherlands

5. Rebekka Wassmann

   Universität Trier

   Affiliation as printed

   Clinical Psychology and Psychotherapy Department, Trier University, Am Wissenschaftspark 25+27, 54296, Trier, Germany

6. Wolfgang Lutz

   Universität Trier

   Affiliation as printed

   Clinical Psychology and Psychotherapy Department, Trier University, Am Wissenschaftspark 25+27, 54296, Trier, Germany

7. Julian A. Rubel

   Osnabrück University

   Affiliation as printed

   Clinical Psychology and Psychotherapy Department, Universität Osnabrück, Lise-Meitner-Str. 3, 49076, Osnabrück, Germany