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.

中文翻译：

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评估用于预测在线治疗中客户情绪的时间因果模型。

背景 在线治疗期间自我报告的客户评估可以开发统计模型来预测客户的改善和症状发展。为了确保其有效性，必须对这些模型进行评估。方法 为此目的，除了基于研究数据的模型评估之外，我们建议使用模拟分析。仿真分析提供了对模型性能的深入了解，并能够分析预测准确性低的原因。在这项研究中，我们评估了时间因果模型（TCM），并表明它不能提供对客户未来情绪水平的可靠预测。结果 基于仿真分析，我们调查了预测性能低的潜在原因，例如噪声测量和采样频率。我们的结论是，目前所分析的中医不足以描述潜在的心理过程。结论 结果证明了模型评估的重要性和模拟分析的好处。当前的手稿为进行模型评估（包括模拟分析）提供了实用指导。