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

Ivair R. Silva ()
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Ivair R. Silva: Federal University of Ouro Preto

Methodology and Computing in Applied Probability, 2018, vol. 20, issue 2, 739-750

Abstract: Abstract 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.

Keywords: Sequential probability ratio test; Expected number of events to signal; Log-exp alpha spending; 62L05; 62L15; 65C05 (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s11009-017-9586-z

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