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Bayesian sequential integration within a preclinical pharmacokinetic and pharmacodynamic modeling framework: Lessons learned.
La Gamba, Fabiola; Jacobs, Tom; Geys, Helena; Jaki, Thomas; Serroyen, Jan; Ursino, Moreno; Russu, Alberto; Faes, Christel.
Afiliação
  • La Gamba F; Department of Quantitative Sciences, Janssen Research & Development, a Division of Janssen Pharmaceutica NV, Beerse, Belgium.
  • Jacobs T; Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Hasselt University, Diepenbeek, Belgium.
  • Geys H; Department of Quantitative Sciences, Janssen Research & Development, a Division of Janssen Pharmaceutica NV, Beerse, Belgium.
  • Jaki T; Department of Quantitative Sciences, Janssen Research & Development, a Division of Janssen Pharmaceutica NV, Beerse, Belgium.
  • Serroyen J; Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Hasselt University, Diepenbeek, Belgium.
  • Ursino M; Department of Mathematics and Statistics, Lancaster University, Lancaster, England.
  • Russu A; Department of Quantitative Sciences, Janssen Research & Development, a Division of Janssen Pharmaceutica NV, Beerse, Belgium.
  • Faes C; Centre de Recherche des Cordeliers, INSERM, Sorbonne Université, USPC, Université Paris Descartes, Université Paris Diderot, Paris, France.
Pharm Stat ; 18(4): 486-506, 2019 07.
Article em En | MEDLINE | ID: mdl-30932327
The present manuscript aims to discuss the implications of sequential knowledge integration of small preclinical trials in a Bayesian pharmacokinetic and pharmacodynamic (PK-PD) framework. While, at first sight, a Bayesian PK-PD framework seems to be a natural framework to allow for sequential knowledge integration, the scope of this paper is to highlight some often-overlooked challenges while at the same time providing some guidances in the many and overwhelming choices that need to be made. Challenges as well as opportunities will be discussed that are related to the impact of (1) the prior specification, (2) the choice of random effects, (3) the type of sequential integration method. In addition, it will be shown how the success of a sequential integration strategy is highly dependent on a carefully chosen experimental design when small trials are analyzed.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Farmacocinética / Ensaios Clínicos como Assunto / Teorema de Bayes / Modelos Biológicos Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Farmacocinética / Ensaios Clínicos como Assunto / Teorema de Bayes / Modelos Biológicos Idioma: En Ano de publicação: 2019 Tipo de documento: Article