不可能的混者:量化COVID-19疫苗有效性的替代解释的极限
Tommaso Costa1,2,3
1GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy.
PloS one
|December 4, 2025
概括
没有测量的混不能完全解释COVID-19疫苗的高效性. 统计分析表明,所需的混因素是不可信的,在观察性研究中支持疫苗的疗效.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 疫苗学 疫苗学 疫苗学
背景情况:
- 观察性研究表明,接种疫苗的个体显著降低了COVID-19风险.
- 对未测量的混存在担忧,这可能解释了这些观察到的关联.
研究的目的:
- 评估单独未测量的混是否可以完全解释观察到的COVID-19疫苗的有效性.
- 评估疫苗有效性的基于混的解释的可信性.
主要方法:
- 与蒙特卡洛灵敏度分析结合的Cornfield不等式.
- 评估了否定观察到的疫苗有效性所需的混强度.
- 在不确定性下模拟的混因子-暴露和混因子-结果关系.
主要成果:
- 对于0.08的风险比率 (Pfizer-BioNTech),混需要极端的失衡和保护作用 (例如,10倍的流行率,99%的风险降低).
- 只有不到2%的模拟符合这些极端混标准.
- 即使风险比为0.25 (AstraZeneca),也只有不到6%的模拟显示出足够的混.
结论:
- 不测量的混不太可能完全解释COVID-19疫苗有效性的观察到的大小.
- 该研究为评估观察性研究中的混提供了一个透明的框架.
- 这些发现支持疫苗有效性估计的统计和流行病学有效性.
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