Evaluation of a temporal causal model for predicting the mood of clients in an online therapy

Dennis Becker, Vincent Bremer, Burkhardt Funk, Mark Hoogendoorn, Artur Rocha, Heleen Riper

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Background Self-reported client assessments during online treatments enable the development of statistical models for the prediction of client improvement and symptom development. Evaluation of these models is mandatory to ensure their validity. Methods For this purpose, we suggest besides a model evaluation based on study data the use of a simulation analysis. The simulation analysis provides insight into the model performance and enables to analyse reasons for a low predictive accuracy. In this study, we evaluate a temporal causal model (TCM) and show that it does not provide reliable predictions of clients' future mood levels. Results Based on the simulation analysis we investigate the potential reasons for the low predictive performance, for example, noisy measurements and sampling frequency. We conclude that the analysed TCM in its current form is not sufficient to describe the underlying psychological processes. Conclusions The results demonstrate the importance of model evaluation and the benefit of a simulation analysis. The current manuscript provides practical guidance for conducting model evaluation including simulation analysis.

Original languageEnglish
Pages (from-to)27-33
Number of pages7
JournalEvidence-Based Mental Health
Volume23
Issue number1
DOIs
Publication statusPublished - 11 Feb 2020

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Keywords

  • mood prediction
  • online treatment
  • predictive modelling
  • temporal causal model

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