用随机变化点对纵向数据进行回归分析.
Peng Zhang1, Xuerong Chen1, Jianguo Sun2
1Center of Statistical Research, School of Statistics, Southwestern University of Finance and Economics, Chengdu, Sichuan, China.
Statistical methods in medical research
|February 24, 2024
概括
这项研究引入了一种新的联合建模方法,用于随机变化点的纵向数据,允许在变化点之前和之后进行特定对象效应和共变异性. 该方法在模拟和现实世界COVID-19数据分析中显示出有效性.
科学领域:
- 统计 统计 统计 统计
- 生物统计学 生物统计学
- 纵向数据分析 纵向数据分析
背景情况:
- 对于具有变化点的纵向数据,现有的回归方法通常将分析限制在连续响应上.
- 目前的方法通常集中在仅影响响应或个体轨迹趋势的变化点上,而不是特定主题的变化.
研究的目的:
- 为纵向数据开发一种新的联合建模方法,以适应特定主体的随机变化点.
- 为了解决变化点前后发生的共同变量的效果异质性.
主要方法:
- 结合了通用线性混合效应模型的纵向反应与随机变化点.
- 集成一个逻辑线性回归模型来处理随机变化点.
- 采用最大概率估计程序进行推断.
主要成果:
- 拟议的方法允许特定主体的变化点和不同的协变量效应.
- 建立了估计器的非对称性质,与标准结果不同.
- 模拟研究表明该方法的实际实用性.
结论:
- 这种新的联合建模方法有效地处理带有随机,主体特定变化点和共变异性的纵向数据.
- 该方法通过模拟进行验证,并应用于COVID-19数据,显示其实际适用性.
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