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A Bayesian phase I-II clinical trial design to find the biological optimal dose on drug combination.
Wang, Ziqing; Zhang, Jingyi; Xia, Tian; He, Ruyue; Yan, Fangrong.
Afiliación
  • Wang Z; Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, P.R. China.
  • Zhang J; Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, P.R. China.
  • Xia T; Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, P.R. China.
  • He R; Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, P.R. China.
  • Yan F; Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, P.R. China.
J Biopharm Stat ; : 1-14, 2023 Jul 17.
Article en En | MEDLINE | ID: mdl-37461311
ABSTRACT
In recent years, combined therapy shows expected treatment effect as they increase dose intensity, work on multiple targets and benefit more patients for antitumor treatment. However, dose -finding designs for combined therapy face a number of challenges. Therefore, under the framework of phase I-II, we propose a two-stage dose -finding design to identify the biologically optimal dose combination (BODC), defined as the one with the maximum posterior mean utility under acceptable safety. We model the probabilities of toxicity and efficacy by using linear logistic regression models and conduct Bayesian model selection (BMS) procedure to define the most likely pattern of dose-response surface. The BMS can adaptively select the most suitable model during the trial, making the results robust. We investigated the operating characteristics of the proposed design through simulation studies under various practical scenarios and showed that the proposed design is robust and performed well.
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Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: J Biopharm Stat Asunto de la revista: FARMACOLOGIA Año: 2023 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: J Biopharm Stat Asunto de la revista: FARMACOLOGIA Año: 2023 Tipo del documento: Article