探索因果机制,并使用复发性和终端事件的联合建模方法量化直接和间接影响
Fang Niu1, Cheng Zheng1, Lei Liu2
1Department of Biostatistics, University of Nebraska Medical Center, Omaha, Nebraska, USA.
Statistics in medicine
|July 18, 2023
概括
这项研究引入了对复发性和终端事件的新因果调解分析. 该方法量化了暴露如何通过中间反复事件影响结果,改进了生存分析.
科学领域:
- 生物统计学 生物统计学
- 流行病学 流行病学
- 因果推理因果推理
背景情况:
- 复发性事件在生物医学研究中很常见,通常受到死亡等终端事件的影响.
- 现有的联合模型与复发性和终端事件相关,但缺乏因果机制调查的方法.
- 了解因果途径对于评估对复发事件进行干预以改善生存结果至关重要.
研究的目的:
- 为复发性和终端事件开发正式的因果调解分析.
- 通过反复发生的事件量化暴露对终端事件的自然直接和间接影响.
- 在联合建模中放松严格的"顺序无视性"假设.
主要方法:
- 提出了一种新的联合建模方法,将重复事件过程作为调解器,并将生存终点作为结果.
- 开发了一个正式的因果调解分析框架,以估计自然的直接和间接影响.
- 利用模拟研究来评估拟议模型的有限样本性能.
主要成果:
- 新的联合建模方法在模拟中对模型参数和调解效应的估计表现良好.
- 该方法成功地放松了常用的"顺序无视性"假设.
- 因果调解分析提供了一种严格的方式来调查暴露-调解者-结果关系.
结论:
- 拟议的因果调解分析提供了一种可靠的方法,用于联合建模复发性和终端事件.
- 这种方法可以更深入地了解反复发生的事件如何调解暴露对生存的影响.
- 应用到艾滋病研究中证明了其在评估治疗有效性和机会性感染在生存结果中的作用方面的实用性.
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