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A Bayesian three-parameter logistic model for early- and late-onset DLTs in oncology Phase I studies.
Zheng, Wei; Zhao, Yang; Lu, Yuefeng; Miao, Harry; Liu, Hengchang.
Afiliação
  • Zheng W; a Sanofi Biostatistics, Sanofi-Aventis US LLC , Cambridge , Massachusetts , USA.
  • Zhao Y; a Sanofi Biostatistics, Sanofi-Aventis US LLC , Cambridge , Massachusetts , USA.
  • Lu Y; a Sanofi Biostatistics, Sanofi-Aventis US LLC , Cambridge , Massachusetts , USA.
  • Miao H; b Incyte Corporation , Wilmington , Delaware , USA.
  • Liu H; c Department of Computer Science , University of Science and Technology of China , Suzhou , Jiangsu , China.
J Biopharm Stat ; 26(2): 339-51, 2016.
Article em En | MEDLINE | ID: mdl-25629564
ABSTRACT
We introduce a three-parameter logistic model to analyze the dose limiting toxicity (DLT) as a time-to-event endpoint in oncology Phase I trials. In the proposed model, patients are allowed to stay on trial without the constraint of a maximum follow-up time. Our model accommodates late-onset DLT as well as early-onset DLT, by both of which the dose recommendation is informed. A Bayesian approach is used to incorporate prior knowledge of the test treatment into dose recommendation. Simulation examples show that our proposed model has good operating characteristics in assessing the maximum tolerated dose (MTD).
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Modelos Logísticos / Teorema de Bayes / Ensaios Clínicos Fase I como Assunto / Oncologia / Antineoplásicos Tipo de estudo: Guideline / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2016 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Modelos Logísticos / Teorema de Bayes / Ensaios Clínicos Fase I como Assunto / Oncologia / Antineoplásicos Tipo de estudo: Guideline / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2016 Tipo de documento: Article