Abstract Sequential analysis is now commonly used for post-market drug and vaccine safety surveillance, and a Poisson stochastic process is typically used for rare adverse events. The conditional maximized sequential probability ratio test, CMaxSPRT, is a powerful tool when there is uncertainty in the estimated expected counts under the null hypothesis. This paper derives exact critical values for CMaxSPRT, as well as statistical power and expected time to signal. This is done for both continuous and group sequential analysis, and for different rejection boundaries. It is also shown how to adjust for covariates in the sequential design. A table of critical values is provided for selected parameters and rejection boundaries, while new functions in the R Sequential package can be used for other calculations. In addition, the method is illustrated for monitoring adverse events after pediarix vaccination data.

中文翻译：

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针对协变量调整的精确条件最大化序贯概率比检验


摘要 序贯分析目前常用于上市后药物和疫苗的安全性监测，而泊松随机过程通常用于罕见的不良事件。当原假设下估计的预期计数存在不确定性时，条件最大化序贯概率比检验 (CMaxSPRT) 是一个强大的工具。本文推导了 CMaxSPRT 的精确临界值，以及统计功效和预期信号时间。这是针对连续分析和组序贯分析以及不同的拒绝边界而完成的。它还展示了如何在序贯设计中调整协变量。为选定的参数和拒绝边界提供了临界值表，而 R Sequential 包中的新函数可用于其他计算。此外，还说明了该方法用于监测 pediarix 疫苗接种数据后的不良事件。