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

Publikation: Beiträge in ZeitschriftenZeitschriftenaufsätzeForschungbegutachtet

Authors

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.

OriginalspracheEnglisch
ZeitschriftBMJ mental health
Jahrgang23
Ausgabenummer1
Seiten (von - bis)27-33
Anzahl der Seiten7
ISSN1362-0347
DOIs
PublikationsstatusErschienen - 11.02.2020

Bibliographische Notiz

Funding Information:
Funding The European Comparative Effectiveness Research on Internet-based Depression Treatment (E-COMPARED) is a project with funding from the European Union Seventh Framework Programme (grant agreement No: 603098).

Publisher Copyright:
© Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.

DOI

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Forschende

  1. Christopher Weigand

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