应用随机截取交叉滞后面板模型到行为干预结果的应用:一种方法论教程,评估模型适合性
Julián D Moreno-Villamizar1, Daniel A Teplow2, Qimin Liu2
1Department of Psychological and Brain Sciences, Center for Anxiety and Related Disorders, Boston University, Boston, USA. jdmoreno@bu.edu.
Journal of behavioral medicine
|December 19, 2025
概括
随机拦截交叉滞后面板模型 (RICLPM) 与传统模型相比,可以更好地了解行为干预期间对象内部的变化. 这种方法增强了对个性化护理治疗期间心理过程的理解.
科学领域:
- 纵向数据分析的数据分析.
- 行为医学是一种行为医学.
- 心理干预研究心理学干预研究
背景情况:
- 随机交叉滞后面板模型 (RICLPM) 越来越多地被使用,因为它能够在纵向数据中分离主体内和主体之间的差异.
- 传统的交叉滞后面板模型 (CLPMs) 可能无法准确地代表随时间推移的动态过程,特别是在干预研究中.
- 对行为干预数据的RICLPM的应用仍未得到充分探索.
研究的目的:
- 展示RICLPM在分析数字行为干预试验 (iUP) 数据中的应用.
- 在使用RICLPM的认知行为治疗期间检查动态心理过程.
- 为调整RICLPM与干预结果数据提供一个方法教程.
主要方法:
- 将RICLPM应用于数字统一协议 (iUP) 临床试验的数据.
- 对RICLPM和传统CLPM之间的模型匹配统计进行了比较.
- 在认知行为干预期间,在心理过程的背景下解释结果.
主要成果:
- 与CLPM相比,RICLPM显示出更好的模型匹配.
- 在干预期间,RICLPM提供了对主体内部过程的更精确估计.
- 该模型有效地捕捉了整个治疗过程中的动态心理变化.
结论:
- RICLPM提供了一种更准确,更强大的方法来分析行为干预的纵向数据.
- 在行为医学中采用RICLPM可以改善心理机制的识别,并帮助个性化干预.
- 这项研究为寻求干预结果的先进纵向分析技术的研究人员提供了宝贵的资源.
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