两个阶段的反复事件随机效应模型
1Section of Biostatistics, Department of Public Health, University Of Copenhagen, Øster farimagsgade 5, DK-1014, Copenhagen, Denmark. thsc@sund.ku.dk.
Lifetime data analysis
|December 29, 2025
概括
本研究引入了新的半参数随机效应模型,用于与终端事件一起分析反复事件. 这些模型有效地捕获依赖关系,而不需要调整参数,提供了强大的统计方法.
科学领域:
- 生物统计学 生物统计学
- 生存分析的分析.
- 统计建模 统计建模
背景情况:
- 在纵向研究中,复发性事件和终端事件经常一起发生.
- 现有的模型可能无法完全捕捉这些事件类型之间的复杂依赖结构.
研究的目的:
- 开发和评估半参数随机效应模型,用于在终端事件存在时的反复事件.
- 使用共享的随机效应,模拟反复和终端事件之间的依赖关系.
主要方法:
- 使用比例边际速率或平均值模型用于反复发生的事件和终端事件的比例模型.
- 制定了允许完全或部分共享随机效应的两阶段模型.
- 采用参数估计程序,避免调整参数和数值集成.
- 标准错误是使用引导计算的.
主要成果:
- 拟议的估计程序在数值上稳定且有效.
- 这些模型成功地捕捉了反复和终端事件之间的依赖关系.
- 这些方法使用来自台中腹腔透析研究的数据进行了验证.
结论:
- 开发的半参数随机效应模型为分析具有终端事件的反复事件提供了灵活和高效的框架.
- 两阶段估计方法为需要数字集成的方法提供了切实可行的替代方案.
- 对透析患者数据的应用证明了模型在现实世界健康研究中的实用性.
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