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通过模拟和案例研究重新审视危险比率的危险性
Michal Abrahamowicz1,2, Marie-Eve Beauchamp3, Emily K Roberts4
1Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada. michal.abrahamowicz@mcgill.ca.
European journal of epidemiology
|July 3, 2025
概括
在时间到事件数据分析中对危险比率 (HR) 限制的担忧可能被夸大了. 模拟表明,未测量的敏感性很少解释HRs的下降,这表明生物因素更有可能. 研究人员应该模拟时间依赖的HRs.
科学领域:
- 生物统计学 生物统计学
- 流行病学 流行病学
- 生存分析的分析.
背景情况:
- 危险比率 (HR) 是时间与事件数据分析中的一个关键指标,可以量化随时间推移的治疗效应.
- 一个批评建议降低HRs可能源于由于未测量的敏感性而导致的选择偏差,而不是真正的治疗变化.
- 这份批评强调了一项具有交叉危险的激素治疗试验,暗示HR可能会被误解.
研究的目的:
- 系统地调查未测量的易感性是否可以解释时间变化的危险比率 (HRs).
- 评估关于HR解释在时间到事件数据中的批评的有效性.
- 探索减少HRs的替代解释,并促进使用依赖时间的HRs.
主要方法:
- 利用模拟研究来建模易感性对随时间的危险比率 (HRs) 的影响.
- 从激素治疗试验中复制的场景,使用模拟来测试敏感性假设.
- 分析了现实世界的案例研究,特别是癌症研究,以证明可解释的依赖时间的HRs.
主要成果:
- 模拟结果表明,由于未测量的易感性,对零值的显著偏差不太可能,除非易感性非常强.
- 模拟激素治疗试验的模拟表明,仅仅是未测量的敏感性是观察到的交叉危险的不太可能解释.
- 现实世界癌症数据提供了临床上可信和可解释的时间依赖性危险比率的例子.
结论:
- 对危险比率 (HRs) 的限制的担忧可能是夸张的.
- 在大多数情况下,未测量的敏感性是减少HR和交叉危险的不太可能的主要驱动因素.
- 鼓励研究人员建模时间依赖的HR,并研究可能影响治疗效果随时间推移的生物学或临床因素.
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