Bayesian sequential integration within a preclinical pharmacokinetic and pharmacodynamic modeling framework: Lessons learned. (1st April 2019)

• Record Type: Journal Article
• Authors: La Gamba, Fabiola; Jacobs, Tom; Geys, Helena; Jaki, Thomas; Serroyen, Jan; Ursino, Moreno; Russu, Alberto; Faes, Christel
• Is Part Of: Pharmaceutical statistics. Volume 18:Number 4(2019)
• Page Start: 486
• Page End: 506
• Publication Date: 2019-04-01
• Subjects: Bayesian inference -- nonlinear hierarchical models -- pharmacodynamics -- pharmacokinetics -- recursive -- sequential; Pharmacy -- Statistical methods -- Periodicals; Pharmacy -- Statistics -- Periodicals; 615.10727
• DOI: 10.1002/pst.1941
• Languages: English
• ISSNs: 1539-1604

Abstract

Abstract : 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.