用灵活的共享随机效应进行重复测量和竞争风险数据的联合模型
1Center for Interdisciplinary Mathematical Sciences (CIMS), Institute of Science, Banaras Hindu University, Varanasi, India.
Journal of biopharmaceutical statistics
|February 12, 2026
概括
这项研究引入了一种新的联合模型,用于分析纵向和竞争性风险数据. 该模型有效地整合了重复测量和时间到事件结果,为临床试验分析提供了更好的洞察力.
科学领域:
- 生物统计学 生物统计学
- 临床试验 临床试验
- 纵向数据分析 纵向数据分析
背景情况:
- 临床试验产生复杂的数据,包括纵向测量和具有竞争风险的时间到事件结果.
- 现有的模型往往难以同时有效地分析这些不同的数据类型.
研究的目的:
- 开发和评估一个共同的统计模型,用于分析纵向数据以及与竞争风险相匹配的时间到事件数据.
- 为推断共变量,纵向轨迹和事件发生之间的关系提供一个强大的框架.
主要方法:
- 一个联合模型,将纵向数据的线性混合效应模型和竞争风险的概括指数分布相结合.
- 一个共享的随机效应结构连接了纵向和生存过程.
- 通过使用预期-最大化算法通过最大概率进行参数估计.
主要成果:
- 拟议的联合模型证明了分析复杂的临床试验数据的可行性和实用性.
- 模拟研究证实了模型在参数估计和推断方面的表现.
- 对SANAD试验数据的应用突出显示了其实际适用性.
结论:
- 开发的联合模型为在临床研究中同时分析纵向和竞争性风险数据提供了一个强大的工具.
- 这种方法提高了对多种事件类型存在的疾病进展和治疗效果的理解.
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