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Article Dans Une Revue Pharmaceutical Statistics Année : 2019

Bayesian sequential integration within a preclinical pharmacokinetic and pharmacodynamic modeling framework: Lessons learned

Fabiola La Gamba
Tom Jacobs
  • Fonction : Auteur
Helena Geys
  • Fonction : Auteur
Jan Serroyen
  • Fonction : Auteur
Moreno Ursino
Alberto Russu
Christel Faes
  • Fonction : Auteur

Résumé

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 aswell 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.

Dates et versions

hal-03873772 , version 1 (27-11-2022)

Identifiants

Citer

Fabiola La Gamba, Tom Jacobs, Helena Geys, Thomas Jaki, Jan Serroyen, et al.. Bayesian sequential integration within a preclinical pharmacokinetic and pharmacodynamic modeling framework: Lessons learned. Pharmaceutical Statistics, 2019, pst.1941. ⟨10.1002/pst.1941⟩. ⟨hal-03873772⟩
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