Predicting personalized process-outcome associations in psychotherapy using machine learning approaches—A demonstration

Julian A. Rubel, Sigal Zilcha-Mano, Julia Giesemann, Jessica Prinz, Wolfgang Lutz

Research output: Contribution to journalArticlepeer-review

Abstract

Objective: Personalized treatment methods have shown great promise in efficacy studies across many fields of medicine and mental health. Little is known, however, about their utility in process-outcome research. This study is the first to apply personalized treatment methods in the field of process-outcome research, as demonstrated based on the alliance-outcome association. Method: Using a sample of 741 patients, individual regressions were fitted to estimate within-patient effects of the alliance-outcome association. The Boruta algorithm was used to identify patient intake characteristics that moderate the within-patient alliance-outcome association. The nearest neighbor approach was used to identify patients whose relevant pretreatment characteristics were similar to those of a target patient. The alliance-outcome associations of the most similar patients were subsequently used to predict the alliance-outcome association of the target patient. Results: Irrespective of the number of selected nearest neighbors, the correlation between the observed and predicted alliance-outcome associations was low and insignificant. According to the true error of the prediction, the demonstrated approach was unable to improve predictions made with a simple comparison model. Conclusion: The study demonstrated the application of personalized treatment methods in process-outcome research and opens many new paths for future research.

Original languageEnglish
Pages (from-to)300-309
Number of pages10
JournalPsychotherapy Research
Volume30
Issue number3
DOIs
StatePublished - 2 Apr 2020

Bibliographical note

Funding Information:
This work was supported by grants from the German Research Foundation (LU 660/10-1, LU 660/8-1). This work was supported by grants from the German Research Foundation (LU 660/10-1, LU 660/8-1). We thank Kaitlyn Boyle for proofreading earlier versions of this manuscript.

Publisher Copyright:
© 2019, © 2019 Society for Psychotherapy Research.

Keywords

  • alliance-outcome research
  • longitudinal data
  • moderators of alliance-outcome association
  • nearest neighbor
  • personalized mental health
  • within- and between-patients effects

ASJC Scopus subject areas

  • Clinical Psychology

Fingerprint

Dive into the research topics of 'Predicting personalized process-outcome associations in psychotherapy using machine learning approaches—A demonstration'. Together they form a unique fingerprint.

Cite this