Li Rongxia, Stewart Brock, Weintraub Eric, McNeil Michael M
Immunization Safety Office, Division of Healthcare Quality Promotion, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, GA, U.S.A.
Stat Med. 2014 Aug 30;33(19):3387-97. doi: 10.1002/sim.6161. Epub 2014 Apr 1.
Various recently developed sequential methods have been used to detect signals for post-marketing surveillance in drug and vaccine safety. Among these, the maximized sequential probability ratio test (MaxSPRT) has been used to detect elevated risks of adverse events following vaccination using large healthcare databases. However, a limitation of MaxSPRT is that it only provides a time-invariant flat boundary. In this study, we propose the use of time-varying boundaries for controlling how type I error is distributed throughout the surveillance period. This is especially useful in two scenarios: (i) when we desire generally larger sample sizes before a signal is generated, for example, when early adopters are not representative of the larger population; and (ii) when it is desired for a signal to be generated as early as possible, for example, when the adverse event is considered rare but serious. We consider four specific time-varying boundaries (which we call critical value functions), and we study their statistical power and average time to signal detection. The methodology we present here can be viewed as a generalization or flexible extension of MaxSPRT.
最近开发的各种序贯方法已被用于药物和疫苗安全性的上市后监测信号检测。其中，最大化序贯概率比检验（MaxSPRT）已被用于利用大型医疗数据库检测疫苗接种后不良事件风险升高的情况。然而，MaxSPRT的一个局限性在于它只提供一个时不变的平坦边界。在本研究中，我们建议使用时变边界来控制第一类错误在整个监测期内的分布方式。这在两种情况下特别有用：（i）当我们在信号产生之前通常希望有更大的样本量时，例如，当早期采用者不能代表更大的人群时；（ii）当希望尽早产生信号时，例如，当不良事件被认为罕见但严重时。我们考虑了四个特定的时变边界（我们称之为临界值函数），并研究了它们的统计功效和信号检测的平均时间。我们在此提出的方法可以被视为MaxSPRT的一种推广或灵活扩展。
