Silva, Ivair Ramos2018-02-012018-02-012017SILVA, I. R. Type I error probability spending for post-market drug and vaccine safety surveillancewith poisson data. Methodology and Computing in Applied Probability, v.01, p.1-12, 2017. Disponível em: <https://link.springer.com/article/10.1007/s11009-017-9586-z>. Acesso em: 16 jan. 2018.1573-7713http://www.repositorio.ufop.br/handle/123456789/9402Statistical 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.en-USrestritoSequential probability ratio testExpected number of events to signalLog-exp alpha spendingArtigo publicado em periodicohttps://link.springer.com/article/10.1007/s11009-017-9586-zhttps://doi.org/10.1007/s11009-017-9586-z