Deep historical borrowing framework to prospectively and simultaneously synthesize control information in confirmatory clinical trials with multiple endpoints.

Zhan, Tianyu; Zhou, Yiwang; Geng, Ziqian; Gu, Yihua; Kang, Jian; Wang, Li; Huang, Xiaohong; Slate, Elizabeth H

Zhan, Tianyu; Zhou, Yiwang; Geng, Ziqian; Gu, Yihua; Kang, Jian; Wang, Li; Huang, Xiaohong; Slate, Elizabeth H.

Afiliação

Zhan T; Data and Statistical Sciences, AbbVie Inc, North Chicago, Illinois, USA.
Zhou Y; Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee, USA.
Geng Z; Data and Statistical Sciences, AbbVie Inc, North Chicago, Illinois, USA.
Gu Y; Data and Statistical Sciences, AbbVie Inc, North Chicago, Illinois, USA.
Kang J; Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.
Wang L; Data and Statistical Sciences, AbbVie Inc, North Chicago, Illinois, USA.
Huang X; Data and Statistical Sciences, AbbVie Inc, North Chicago, Illinois, USA.
Slate EH; Department of Statistics, Florida State University, Tallahassee, Florida, USA.

J Biopharm Stat ; 32(1): 90-106, 2022 01 02.

Article em En | MEDLINE | ID: mdl-34632951

Assuntos

Projetos de Pesquisa; Teorema de Bayes; Simulação por Computador; Humanos; Probabilidade; Tamanho da Amostra

Palavras-chave

Bayesian hierarchical model; deep learning; family-wise error rate control; power preservation; prospective algorithm

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Projetos de Pesquisa Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo

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XML

PubMed Links

Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Projetos de Pesquisa Idioma: En Ano de publicação: 2022 Tipo de documento: Article