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dc.contributor.authorSilva, Ivair Ramos-
dc.contributor.authorKulldorff, Martin-
dc.contributor.authorYih, W. Katherine-
dc.date.accessioned2022-03-03T19:00:42Z-
dc.date.available2022-03-03T19:00:42Z-
dc.date.issued2020pt_BR
dc.identifier.citationSILVA, I. R.; KULLDORF, M.; YIH, W. K. Optimal alpha spending for sequential analysis with binomial data. Journal of the Royal Statistical Society Series B-Statistical Methodology, v. 82, n. 4, p. 1141-1164, 2020. Disponível em: <https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/rssb.12379>. Acesso em: 25 ago. 2021.pt_BR
dc.identifier.issn1467-9868-
dc.identifier.urihttp://www.repositorio.ufop.br/jspui/handle/123456789/14622-
dc.description.abstractFor sequential analysis hypothesis testing, various alpha spending functions have been proposed. Given a prespecified overall alpha level and power, we derive the optimal alpha spending function that minimizes the expected time to signal for continuous as well as group sequential analysis. If there is also a restriction on the maximum sample size or on the expected sample size, we do the same. Alternatively, for fixed overall alpha, power and expected time to signal, we derive the optimal alpha spending function that minimizes the expected sample size. The method constructs alpha spending functions that are uniformly better than any other method, such as the classical Wald, Pocock or O’Brien–Fleming methods.The results are based on exact calculations using linear programming. All numerical examples were run by using the R Sequential package.pt_BR
dc.language.isoen_USpt_BR
dc.rightsrestritopt_BR
dc.subjectClinical trialspt_BR
dc.subjectExpected time to signalpt_BR
dc.subjectO’Brien–Fleming testpt_BR
dc.subjectPocock testpt_BR
dc.subjectSafety surveillancept_BR
dc.titleOptimal alpha spending for sequential analysis with binomial data.pt_BR
dc.typeArtigo publicado em periodicopt_BR
dc.identifier.uri2https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/rssb.12379pt_BR
dc.identifier.doihttps://doi.org/10.1111/rssb.12379pt_BR
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