Type I Error Probability Spending for Post-Market Drug and Vaccine Safety Surveillance With Poisson Data

  • Ivair R. Silva


Statistical sequential hypothesis testing is meant to analyze cumulative data accruing in time. The methods can be divided in two types, group and continuous sequential approaches, and a question that arises is if one approach suppresses the other in some sense. For Poisson stochastic processes, we prove that continuous sequential analysis is uniformly better than group sequential under a comprehensive class of statistical performance measures. Hence, optimal solutions are in the class of continuous designs. This paper also offers a pioneer study that compares classical Type I error spending functions in terms of expected number of events to signal. This was done for a number of tuning parameters scenarios. The results indicate that a log-exp shape for the Type I error spending function is the best choice in most of the evaluated scenarios.


Sequential probability ratio test Expected number of events to signal Log-exp alpha spending 

Mathematics Subject Classification (2010)

62L05 62L15 65C05 


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Supplementary material

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of StatisticsFederal University of Ouro PretoOuro PretoBrazil

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