一个规范的连续时间隐藏的马尔科夫模型,用于识别多烟草使用的潜在状态过渡模式
Xinyu Yan1, Ji-Hyun Lee1,2, Xiang-Yang Lou1
1Department of Biostatistics, University of Florida, Gainesville, FL 32611, United States.
Biometrics
|October 15, 2025
概括
这项研究引入了一个新的连续时间隐藏马尔科夫模型 (HMM) 来分析复杂的烟草使用模式. 该模型有效地识别风险因素,并确定纵向数据中隐藏状态的数量.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 公共卫生 公共卫生
背景情况:
- 隐藏的马尔科夫模型 (HMM) 是分析物质使用模式的标准.
- 传统的HMM与烟草使用研究中常见的高维数据和不规则的时间间隔作斗争.
- 准确识别潜伏状态和风险因素对于理解多烟草使用过渡至关重要.
研究的目的:
- 开发一种新的连续时间HMM框架,用于分析复杂的多烟草使用过渡.
- 解决传统HMM在处理高维度风险因素和不同时间间隔方面的局限性.
- 确定影响烟草使用国家之间的过渡的关键人口,行为和心理社会风险因素.
主要方法:
- 提出了一个连续时间的HMM,用于过渡共变量的弹性网调整.
- 纳入调查权重,层和聚类到建模框架中.
- 通过模拟验证方法来确定状态数,共变量选择和参数估计.
主要成果:
- 拟议的HMM框架准确地确定了隐藏状态的数量.
- 弹性网规范化成功确定了过渡参数的信息共变量.
- 模拟证实了该模型在参数估计和共变量选择中的有效性.
- 应用到PATH研究数据揭示了与青少年和年轻成年人吸烟过渡有关的显著的人口,行为和心理社会因素.
结论:
- 具有规范化的连续时间HMM为分析复杂的多烟草使用提供了强大的方法.
- 该模型有效地识别了纵向物质使用数据中的高维风险因素和潜在变量.
- 这种方法对推进公共卫生研究和为烟草使用提供有针对性的干预措施提供了重大潜力.
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