一个贝叶斯联合纵向生存模型,用于密集纵向数据的潜在随机过程
Madeline R Abbott1, Walter H Dempsey1, Inbal Nahum-Shani2
1Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, United States.
Biometrics
|May 5, 2025
概括
这项研究引入了一种新的统计模型,用于分析来自移动健康 (mHealth) 研究的密集纵向数据 (ILD). 该模型有效地捕捉了情绪状态的变化如何影响戒烟成功和复发风险.
科学领域:
- 生物统计学 生物统计学
- 数字健康数字健康
- 行为科学 行为科学
背景情况:
- 移动健康 (mHealth) 技术促进了密集的纵向数据 (ILD) 收集,为动态的健康结果提供了洞察力.
- 对于纵向和事件时间数据的现有联合模型与ILD的复杂性和计算需求作斗争.
研究的目的:
- 为分析密集的纵向数据 (ILD) 提议一个新的关节纵向和时间到事件模型.
- 通过模拟和应用到戒烟mHealth研究来评估模型的性能.
主要方法:
- 将多变量纵向结果总结为使用奥恩斯坦-乌伦贝克随机过程的时间变化的潜在因素.
- 在危险模型框架内对时间到事件结果的风险进行参数建模.
- 采用贝叶斯的方法来适应模型和性能评估.
主要成果:
- 拟议的模型有效地分析了ILD,从9种情绪中总结了复杂的情绪状态 (积极和负面影响).
- 发现这些潜伏状态捕捉了戒烟尝试后吸烟失效的风险.
- 该模型证明适合分析来自mHealth戒烟干预措施的数据.
结论:
- 开发的联合模型提供了一种高效和有效的方法,用于分析密集的纵向数据在mHealth研究.
- 了解动态心理状态对于预测和干预吸烟复发至关重要.
- 这种方法推进了数字健康研究中复杂的行为数据的分析.
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