Optimizing Graphical Procedures for Multiplicity Control in a Confirmatory Clinical Trial via Deep Learning

In confirmatory clinical trials, it has been proposed to use a simple iterative graphical approach to construct and perform intersection hypotheses tests with a weighted Bonferroni-type procedure to control Type I errors in the strong sense. Given Phase II study results or other prior knowledge, it...

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Detalles Bibliográficos
Autores principales: Zhan, Tianyu, Hartford, Alan, Kang, Jian, Offen, Walter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8992139/
https://www.ncbi.nlm.nih.gov/pubmed/35401935
http://dx.doi.org/10.1080/19466315.2020.1799855

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8992139/
https://www.ncbi.nlm.nih.gov/pubmed/35401935
http://dx.doi.org/10.1080/19466315.2020.1799855

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