在测试负面设计研究中需要临床案例定义,以估计疫苗有效性
Sheena G Sullivan1,2, Arseniy Khvorov3, Xiaotong Huang4
1WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, and Department of Infectious Diseases, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia. sheena.sullivan@influenzacentre.org.
NPJ vaccines
|August 12, 2023
概括
试验负的研究可以估计COVID-19疫苗的有效性 (VE),但选择偏差可能会膨胀结果. 使用临床病例定义有助于减轻偏见,确保对公共健康进行更准确的 VE 估计.
科学领域:
- 流行病学 流行病学
- 疫苗学 疫苗学 疫苗学
- 生物统计学 生物统计学
背景情况:
- 测试阴性研究是估计COVID-19疫苗有效性的常见的.
- 这些研究依赖于特定的假设来估计VE与医疗监护疾病之间的差异.
- 如果参与概率与疫苗接种状态或感染相关,可能会出现潜在的选择偏差.
研究的目的:
- 调查选择偏差对COVID-19疫苗有效性估计的影响.
- 评估缓解策略,特别是使用临床病例定义.
主要方法:
- 进行了对测试阴性研究的系统审查,重新分析数据以确定遗漏临床标准的数据.
- 根据病例和疫苗接种状况,在不同的选择概率下进行模拟以模拟偏差.
- 为研究人员开发了一个HTML工具,用于评估特定站点的选择偏差.
主要成果:
- 使用临床案例定义的研究与没有使用临床案例定义的研究相比,产生较低的聚合 VE 估计.
- 模拟表明当健康的,接种疫苗的非病例过度代表时,积极偏差 (膨胀的VE) 经常出现在高疫苗接种地区的无症状查数据中.
- 这些发现与系统审查对潜在膨胀的 VE 估计的观察一致.
结论:
- 选择偏差可以显著影响来自测试负结果研究的COVID-19疫苗有效性估计.
- 使用临床病例定义对于减少偏见和获得更可靠的 VE 是至关重要的.
- 研究人员应该主动考虑并解决潜在的选择偏差,特别是在使用行政数据时.
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