使用随机试验数据评估COVID-19疫苗有效性测试负面设计:对5个随机临床试验的二次交叉协议分析
Leah I B Andrews1, M Elizabeth Halloran1,2, Kathleen M Neuzil3
1Department of Biostatistics, School of Public Health, University of Washington, Seattle.
JAMA network open
|May 28, 2025
概括
测试负面设计 (TND) 可靠地评估COVID-19疫苗对症状疾病的有效性. 这种方法与随机临床试验结果一致,为营销后疫苗监测提供了有效的方法.
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:
- 测试负面设计 (TND) 经常用于营销后的COVID-19疫苗有效性研究.
- 对于此应用程序,需要对TND进行进一步验证.
研究的目的:
- 评估TND在评估针对症状性COVID-19的疫苗有效性的可靠性.
- 将TND估计值与来自安慰剂控制随机临床试验 (RCT) 的疫苗疗效数据进行比较.
主要方法:
- 通过使用来自16个国家的5个协调的COVID-19预防网络RCT的3期RCT的数据进行了交叉协议分析.
- 从具有COVID-19类似症状的参与者中构建了TND数据集,这些参与者被测试了SARS-CoV-2.
- 使用目标最大概率估计和普通后勤回归来估计疫苗的有效性,评估非病例可替换性与非COVID-19疾病.
主要成果:
- TND疫苗的有效性估计显示与RCT疫苗有效性估计有很强的一致性 (一致性相关系数:0.86).
- 一种半参数方法导致差异估计比普通逻辑回归小48%.
- 非病例可交换性普遍得到支持,疫苗对非COVID-19疾病的中位数疗效为7.7%.
结论:
- 在寻求医疗保健的人群中,TND为COVID-19疫苗的有效性提供了可靠的推断.
- 当控制混和选择偏差时,TND对多种疫苗和症状定义有效.
- 引入了一种机器学习方法,用于在营销后TND研究中加强混控制.
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