在住院的Covid-19环境中对碰撞器偏差的调整
Moslem Taheri Soodejani1, Seyyed Mohammad Tabatabaei2, Mohammad Hassan Lotfi1
1Center for Healthcare Data Modeling, Departments of Biostatistics and Epidemiology, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.
Global epidemiology
|December 19, 2023
概括
因果定向非循环图 (cDAG) 有助于调整入院COVID-19患者的碰撞器偏差. 考虑到年龄和并发症,疫苗接种对抗COVID-19死亡的保护作用显而易见.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 因果推理因果推理
背景情况:
- 因果定向非循环图 (cDAG) 对于识别混和碰撞器偏差至关重要.
- 这项研究侧重于在住院COVID-19患者的背景下调整碰撞器偏差.
研究的目的:
- 展示cDAG的应用,以减轻COVID-19研究中的碰撞器偏差.
- 研究在住院COVID-19患者的疫苗接种和死亡率之间的关联,同时解决选择偏差.
主要方法:
- 采用了三种统计模型,逐步纳入共变量.
- 模型1:疫苗接种是唯一的独立变量.
- 模型2和3:分别包括年龄和并发症,以调整因住院条件而引入的碰撞器偏差.
主要成果:
- 初步分析 (模型1) 显示疫苗接种对COVID-19死亡率没有显著影响.
- 调整年龄 (模型2) 显示了疫苗接种的保护作用.
- 对伴随性疾病的进一步调整 (模型3) 强化了这种观察到的保护效应.
结论:
- 住院患者队伍容易受到碰撞器分层偏差的影响.
- 这种偏差可以通过在回归模型中包括与结果和选择标准 (住院) 相关的风险因素来有效地管理.
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