In recent years, there has been increasing interest in utilizing time-lagged panel models to study mechanisms of change in psychotherapy. These models offer valuable insights into the dynamic relationships between variables over time and offer stronger causal inference capabilities than cross-sectional analyses. Therefore, they are well-suited for modeling the intricate relationships between mechanisms of change and outcomes in psychotherapy studies, which are typically beyond experimental control. However, their complexity, coupled with the fact that detailed explanations are often embedded in dense statistical or econometric texts, poses challenges. This paper provides a background on cross-lagged panel models and delves deeper into explaining the issues of 1) dynamic panel bias, 2) long-run effects, and 3) testing whether different treatments work by different mechanisms. Using data from a psychotherapy study on treatment of adolescent depression, I demonstrate how these issues manifest in real data. In conclusion, I recommend using structural equation modeling to circumvent dynamic panel bias, reporting long-run effects to reveal the long-term impact of sustained therapeutic work on mechanisms of change, and carefully considering whether mediation, moderation, or a combination of both, best describes differential effects of mechanisms between treatments.

中文翻译：

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心理治疗过程和变化研究机制中的时滞小组模型：方法论的挑战和进展


近年来，人们越来越有兴趣利用时滞面板模型来研究心理治疗的变化机制。这些模型为变量之间随时间变化的动态关系提供了有价值的见解，并提供比横截面分析更强的因果推理能力。因此，它们非常适合对心理治疗研究中变化机制和结果之间的复杂关系进行建模，这些关系通常超出实验控制。然而，它们的复杂性，加上详细的解释往往嵌入在密集的统计或计量经济学文本中，带来了挑战。本文提供了交叉滞后面板模型的背景知识，并深入探讨了以下问题：1) 动态面板偏差，2) 长期效应，以及 3) 测试不同的治疗方法是否通过不同的机制发挥作用。我利用青少年抑郁症治疗心理治疗研究的数据，展示了这些问题在真实数据中的表现。总之，我建议使用结构方程模型来规避动态面板偏差，报告长期效应以揭示持续治疗工作对变化机制的长期影响，并仔细考虑是否采用中介、调节或两者的组合，最能描述治疗之间机制的差异效应。