纵向数据的共享参数建模,允许可能具有信息性的访问过程和终端事件
Christos Thomadakis1,2, Loukia Meligkotsidou2, Nikos Pantazis1
1Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, Mikras Asias 75, Athens, 115 27, Greece.
Biostatistics (Oxford, England)
|October 25, 2024
概括
在联合模型中忽视信息访问流程,可能会导致纵向标记估计偏差. 这项研究提出了一种统一的方法,可以考虑访问过程,提高时间到事件分析的准确性.
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:
- 共享参数模型 (SPM) 共同分析纵向和时间到事件数据,但通常假定非信息访问过程.
- 信息化的访问流程,观察时间取决于患者病史,可能会导致SPM估计偏差.
- 当前的方法通常假定标记,访问和事件过程之间的条件独立性.
研究的目的:
- 开发一种统一的,灵活的方法,共同建模纵向标记,信息访问流程和竞争风险时间到事件数据.
- 评估信息访问流程对联合模型中的参数估计的影响.
- 评估不同访问过程配方 (间隔时间与日历时间) 的稳定性.
主要方法:
- 提出了一种新的联合模型,条件是标记者历史,访问时间和随机效应.
- 该模型适用于访问过程的间隙时间和日历时间尺度.
- 竞争风险被纳入到时间到事件组件中.
- 为了评估性能,进行了广泛的模拟研究.
主要成果:
- 忽视信息访问过程会导致标记估计显著偏差.
- 对访问过程的错误规范也引入了偏见.
- 间隔时间的表述显示出比基于强度的模型对错误规格的更强大的稳定性.
- 在访问过程模型中包括之前的访问历史记录可以提高估计准确性.
结论:
- 信息访问流程至关重要,应在联合分析中明确建模.
- 拟议的统一方法为联合建模提供了一个灵活而强大的框架.
- 访问过程的准确建模对于纵向和生存数据分析中可靠的推断至关重要.
- 该方法已成功应用于HIV纵向数据,访问频率可变.
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