Improving prediction abilities in the therapy process can increase therapeutic success for a variety of reasons, such as more personalised treatment or resource optimisation. The increasingly applied methods of dynamic prediction seem to be very promising for this purpose. Prediction models are usually based on static approaches of frequentist statistics. However, the application of this statistical approach has been widely criticised in this research area. Bayesian statistics has been proposed in the literature as an alternative, especially for the task of dynamic modelling. In this study, we compare the performance of predicting therapy outcome over the course of therapy between both statistical approaches.

中文翻译：

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心理治疗结果的动态和静态预测模型——两种统计方法的比较

提高治疗过程中的预测能力可以出于多种原因提高治疗成功率，例如更加个性化的治疗或资源优化。越来越多地应用的动态预测方法似乎对此很有希望。预测模型通常基于频率统计的静态方法。然而，这种统计方法的应用在该研究领域受到了广泛的批评。贝叶斯统计已在文献中提出作为替代方案，特别是对于动态建模任务。在这项研究中，我们比较了两种统计方法在治疗过程中预测治疗结果的性能。