Using classification trees to identify psychotherapy patients at risk for poor treatment adherence

Abstract

Objective

To determine the relative importance of a wide variety of personality and psychopathology variables in influencing patients’ adherence to psychotherapy treatment.

Method

Two classification trees were trained to predict patients’ (1) treatment utilization (i.e., their likelihood of missing a given appointment) and (2) termination status (i.e., their likelihood of dropping out of therapy prematurely). Each tree was then validated in an external dataset to examine performance accuracy.

Results

Patients’ social detachment was most influential in predicting their treatment utilization, followed by affective instability and activity/energy levels. Patients’ interpersonal warmth was most influential in predicting their termination status, followed by levels of disordered thought and resentment. The overall accuracy rating for the tree for termination status was 71.4%, while the tree for treatment utilization had a 38.7% accuracy rating.

Conclusion

Classification trees are a practical tool for clinicians to determine patients at risk of premature termination. More research is needed to develop trees that predict treatment utilization with high accuracy across different types of patients and settings.

Keywords:

• classification trees
• attendance
• machine learning
• dropout
• psychotherapy

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Supplemental data

Supplemental data for this article can be accessed https://doi.org/10.1080/10503307.2023.2183911.

Additional information

Funding

Manuscript preparation was supported by the NIDA Epidemiology Training Program (#5T32DA007292-30) awarded to the first author.