从外部数据中获取信息的应用,以纠正Covid-19住院队列中的碰撞器偏差
Annastazia Learoyd1, Jennifer Nicholas2, Nicholas Hart3,4
1School of Life Course and Population Sciences, King College London, London, UK. annastazia.learoyd@kcl.ac.uk.
BMC medical research methodology
|July 16, 2024
概括
研究人员使用外部数据纠正了COVID-19研究中的碰撞器偏差. 这种方法准确地评估了少数民族和不良COVID-19结果之间的联系,改善了健康公平研究.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 公共卫生 公共卫生
背景情况:
- 电子健康记录 (EHR) 对COVID-19研究至关重要,但通常通过专注于严重病例来引入碰撞器偏差.
- 碰撞器偏差可能会扭曲研究结果,特别是关于种族和COVID-19结果之间的关系.
- 纠正碰撞器偏差的传统方法需要来自不受限制的人口的数据,这通常是不可用的.
研究的目的:
- 用外部来源的数据展示一种用于纠正COVID-19研究中的碰撞器偏差的新方法.
- 在一组住院患者中检查少数民族与不良COVID-19结果之间的关联.
- 验证使用外部数据在EHR研究中调整选择偏差的可行性.
主要方法:
- 使用已公布的国家COVID-19数据建模住院的概率.
- 基于外部数据的应用反向概率权重 (IPW),以调整对撞机偏差.
- 进行了生存分析,以评估种族作为COVID-19死亡风险因素.
主要成果:
- 不加权分析显示,黑人族裔的死亡风险降低,与现有文献相矛盾.
- IPW的分析纠正了这一异常发现,将结果与已公布的数据保持一致.
- 偏差校正方法在不同的COVID-19波浪和灵敏度分析中被证明是强大的.
结论:
- 可以有效地利用外部数据来纠正入院队列中的碰撞器偏差.
- 这种方法提高了基于EHR的研究的可靠性,特别是对于弱势群体.
- 证明的方法为解决流行病学研究中的选择偏差提供了一个可行的解决方案.
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