贝叶斯联合模型用于纵向,反复和终端事件数据
Emily M Damone1, Matthew A Psioda2, Joseph G Ibrahim3
1Department of Biostatistics - University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill, NC, 27516, USA. edamone@live.unc.edu.
Lifetime data analysis
|October 9, 2025
概括
这项研究引入了一个新的联合模型,同时分析复发性,终端生存事件和纵向数据. 灵活的方法可以考虑这些健康结果之间的依赖性,而没有强烈的相关性假设.
科学领域:
- 生物统计学 生物统计学
- 纵向数据分析 纵向数据分析
- 生存分析的分析.
背景情况:
- 现有的方法通常仅对结果 (例如,生存和纵向数据) 或反复和终端事件进行建模.
- 很少有统计模型能够共同分析复发事件,终端生存事件和纵向结果.
- 当前的方法通常需要对这些不同的数据类型之间的相关性做出强有力的假设.
研究的目的:
- 提出一种新的联合统计模型,能够同时分析复发事件,终端生存事件和纵向结果.
- 开发一个灵活的建模框架,考虑这三种类型的健康事件之间的依赖关系.
- 克服现有方法的局限性,需要强烈的相关性假设.
主要方法:
- 一个结合模型,结合了特定对象的随机效应,以将生存率和纵向结果模型联系起来.
- 具有共同脆弱性的比例危险模型,以捕捉复发性和终端生存事件之间的依赖.
- 一个通用的线性混合模型与相关的随机效应用于纵向数据分析,通过多变量正常分布连接.
主要成果:
- 拟议的联合模型有效地整合了复发事件,终端存活事件和纵向数据.
- 在与生存事件一起建模独特的纵向轨迹方面表现出灵活性.
- 成功应用于来自社区动脉样硬化风险 (ARIC) 研究的现实世界健康数据.
结论:
- 开发的联合建模方法为分析复杂的健康数据提供了强大而灵活的方法.
- 这种方法可以应用于各种健康研究领域,需要同时分析多种事件类型和纵向测量.
- 该模型为了解不同健康结果之间的相互作用提供了有价值的工具.
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