Type I error probability spending for post-market drug and vaccine safety surveillancewith poisson data.
dc.contributor.author | Silva, Ivair Ramos | |
dc.date.accessioned | 2018-02-01T13:45:11Z | |
dc.date.available | 2018-02-01T13:45:11Z | |
dc.date.issued | 2017 | |
dc.description.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. | pt_BR |
dc.identifier.citation | SILVA, 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. | pt_BR |
dc.identifier.doi | https://doi.org/10.1007/s11009-017-9586-z | |
dc.identifier.issn | 1573-7713 | |
dc.identifier.uri | http://www.repositorio.ufop.br/handle/123456789/9402 | |
dc.identifier.uri2 | https://link.springer.com/article/10.1007/s11009-017-9586-z | pt_BR |
dc.language.iso | en_US | pt_BR |
dc.rights | restrito | pt_BR |
dc.subject | Sequential probability ratio test | pt_BR |
dc.subject | Expected number of events to signal | pt_BR |
dc.subject | Log-exp alpha spending | pt_BR |
dc.title | Type I error probability spending for post-market drug and vaccine safety surveillancewith poisson data. | pt_BR |
dc.type | Artigo publicado em periodico | pt_BR |
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