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A Bayesian approach based on discounting factor for consistency assessment in multi-regional clinical trial.
Tong, Liang; Li, Chen; Xia, Jielai; Wang, Ling.
Afiliação
  • Tong L; Department of Health Statistics, Faculty of Preventive Medicine, Air Force Medical University, Xi'an, Shaanxi, China.
  • Li C; Center for Disease Control and Prevention of Central Theater Command, Beijing, China.
  • Xia J; Department of Health Statistics, Faculty of Preventive Medicine, Air Force Medical University, Xi'an, Shaanxi, China.
  • Wang L; Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, Xi'an, Shaanxi, China.
J Biopharm Stat ; : 1-17, 2024 Mar 20.
Article em En | MEDLINE | ID: mdl-38506674
ABSTRACT
Multi-regional clinical trial (MRCT) has become an increasing trend for its supporting simultaneous global drug development. After MRCT, consistency assessment needs to be conducted to evaluate regional efficacy. The weighted Z-test approach is a common consistency assessment approach in which the weighting parameter W does not have a good practical significance; the discounting factor approach improved from the weighted Z-test approach by converting the estimation of W in original weighted Z-test approach to the estimation of discounting factor D. However, the discounting factor approach is an approach of frequency statistics, in which D was fixed as a certain value; the variation of D was not considered, which may lead to un-reasonable results. In this paper, we proposed a Bayesian approach based on D to evaluate the treatment effect for the target region in MRCT, in which the variation of D was considered. Specifically, we first took D random instead of fixed as a certain value and specified a beta distribution for it. According to the results of simulation, we further adjusted the Bayesian approach. The application of the proposed approach was illustrated by Markov Chain Monte Carlo simulation.
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Texto completo: 1 Temas: ECOS / Aspectos_gerais Bases de dados: MEDLINE Idioma: En Revista: J Biopharm Stat Assunto da revista: FARMACOLOGIA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Temas: ECOS / Aspectos_gerais Bases de dados: MEDLINE Idioma: En Revista: J Biopharm Stat Assunto da revista: FARMACOLOGIA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China