Kulldorff Martin, Silva Ivair R
Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, USA.
Department of Statistics, Universidad Federal de Ouro Preto, Ouro Preto, Brazil.
Revstat Stat J. 2017 Jul;15(3):373-394.
The CDC Vaccine Safety Datalink project has pioneered the use of near real-time post-market vaccine safety surveillance for the rapid detection of adverse events. Doing weekly analyses, continuous sequential methods are used, allowing investigators to evaluate the data near-continuously while still maintaining the correct overall alpha level. With continuous sequential monitoring, the null hypothesis may be rejected after only one or two adverse events are observed. In this paper, we explore continuous sequential monitoring when we do not allow the null to be rejected until a minimum number of observed events have occurred. We also evaluate continuous sequential analysis with a delayed start until a certain sample size has been attained. Tables with exact critical values, statistical power and the average times to signal are provided. We show that, with the first option, it is possible to both increase the power and reduce the expected time to signal, while keeping the alpha level the same. The second option is only useful if the start of the surveillance is delayed for logistical reasons, when there is a group of data available at the first analysis, followed by continuous or near-continuous monitoring thereafter.
美国疾病控制与预防中心疫苗安全数据链项目率先利用近乎实时的上市后疫苗安全监测来快速检测不良事件。通过每周进行分析,采用连续序贯方法,使研究人员能够近乎连续地评估数据,同时仍保持正确的总体显著性水平。采用连续序贯监测时,仅观察到一两个不良事件后就可能拒绝原假设。在本文中,我们探讨在不允许在观察到最少数量的事件之前拒绝原假设的情况下进行连续序贯监测。我们还评估了延迟开始直到达到一定样本量后的连续序贯分析。提供了具有精确临界值、统计功效和发出信号平均时间的表格。我们表明,对于第一种选择,在保持显著性水平不变的同时,既可以提高功效又可以减少发出信号的预期时间。第二种选择仅在由于后勤原因监测开始延迟、首次分析时有一组可用数据且此后进行连续或近乎连续监测的情况下才有用。