Wei Qinyu, Wang Peng, Yin Ping
Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Front Public Health. 2022 Aug 12;10:848120. doi: 10.3389/fpubh.2022.848120. eCollection 2022.
This article focuses on the construction of a confidence interval for vaccine efficacy against contagious coronavirus disease-2019 (COVID-19) in a fixed number of events design. Five different approaches are presented, and their performance is investigated in terms of the two-sided coverage probability, non-coverage probability at the lower tail, and expected confidence interval width. Furthermore, the effect of under-sensitivity of diagnosis tests on vaccine efficacy estimation was evaluated. Except for the exact conditional method, the non-coverage probability of the remaining methods may exceed the nominal significance level, e.g., 5%, even for a large number of total confirmed COVID-19 cases. The narrower confidence interval width from the Bayesian, approximate Poisson, and mid-P methods are on the cost of increased instability of coverage probability. When the sensitivity of diagnosis test in the vaccine group is lower than that in the placebo group, the reported vaccine efficacy tends to be overly optimistic. The exact conditional method is preferable to other methods in COVID-19 vaccine efficacy trials when the total number of cases reaches 60; otherwise, mid-p method can be used to obtain a narrower interval width.
本文重点关注在固定事件数设计中针对传染性冠状病毒病2019（COVID-19）的疫苗效力置信区间的构建。介绍了五种不同的方法，并从双侧覆盖概率、下尾非覆盖概率和预期置信区间宽度方面研究了它们的性能。此外，评估了诊断检测灵敏度不足对疫苗效力估计的影响。除了精确条件法外，即使对于大量确诊的COVID-19病例，其余方法的非覆盖概率也可能超过名义显著性水平，例如5%。贝叶斯法、近似泊松法和中P法得到的置信区间宽度较窄，但代价是覆盖概率的不稳定性增加。当疫苗组诊断检测的灵敏度低于安慰剂组时，报告的疫苗效力往往过于乐观。当病例总数达到60时，在COVID-19疫苗效力试验中，精确条件法优于其他方法；否则，可以使用中P法获得更窄的区间宽度。
