在行为研究中分析随机预测后测试随访试验的纵向数据的方法:潜在变化模型的实用指南
1Behavioral Medicine and Clinical Psychology, Cincinnati Children's Hospital Medical Center, Department of Pediatrics, University of Cincinnati College of Medicine, 3333 Burnet Avenue, MLC 7039, Cincinnati, OH, 45229, USA. Constance.Mara@cchmc.org.
Journal of behavioral medicine
|September 9, 2025
概括
潜变模型 (LCM) 提供了一种强大的方法来分析具有多个时间点的纵向行为干预研究. 这些模型准确地估计了随机预测-测试后跟踪 (RPPF) 设计中的治疗效应和随时间变化.
科学领域:
- 行为科学 行为科学
- 临床心理学 临床心理学
- 生物统计学 生物统计学
背景情况:
- 随机预测后测试后跟踪 (RPPF) 设计是评估纵向行为干预措施的标准.
- 在这些设计中,评估治疗疗效和随着时间的推移持续的影响至关重要.
- 传统的分析方法可能无法完全捕捉RPPF试验中变化的细微差别.
研究的目的:
- 引入潜变模型 (LCM) 作为RPPF试验的实用分析方法.
- 用STAR试验的数据来证明LCMs的实用性,用于儿科的坚持.
- 将LCM与ANCOVA和混合效应模型等传统方法进行比较.
主要方法:
- 应用潜变模型 (LCM) 来分析RPPF数据.
- 利用了STAR (支持治疗坚持方案) 试验数据,这是一项儿科行为干预研究.
- 用ANCOVA,纵向线性混合效应模型和潜增长曲线模型对比LCM结果.
主要成果:
- LCM有效地估计了时间点之间的离散变化和干预对照组差异.
- 分析表明,LCM能够控制基线变化,并整合所有纵向数据.
- 与其他方法相比,LCM提供了更准确,更细致的干预效应理解.
结论:
- 隐性变化模型 (LCM) 是分析行为干预研究中RPPF试验的强大工具.
- LCM在估计随时间推移的特定变化和处理复杂的纵向数据方面具有优势.
- 这些模型增强了对干预效率及其时间动态的理解.
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