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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/29072

Title: Bayesian sequential integration within a preclinical pharmacokinetic and pharmacodynamic modeling framework: Lessons learned
Authors: La Gamba, Fabiola
Jacobs, Tom
Geys, Helena
Jaki, Thomas
Serroyen, Jan
Ursino, Moreno
Russu, Alberto
Faes, Christel
Issue Date: 2019
Publisher: WILEY
Citation: PHARMACEUTICAL STATISTICS, 18 (4), p. 486-506
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.
Notes: [La Gamba, Fabiola; Jacobs, Tom; Geys, Helena; Serroyen, Jan; Russu, Alberto] Janssen Res & Dev, Dept Quantitat Sci, Beerse, Belgium. [La Gamba, Fabiola; Geys, Helena; Faes, Christel] Hasselt Univ, Interuniv Inst Biostat & Stat Bioinformat, Diepenbeek, Belgium. [Jaki, Thomas] Univ Lancaster, Dept Math & Stat, Lancaster, England. [Ursino, Moreno] Univ Paris Diderot, Univ Paris Descartes, Sorbonne Univ, Ctr Rech Cordeliers,INSERM,USPC, Paris, France.
URI: http://hdl.handle.net/1942/29072
DOI: 10.1002/pst.1941
ISI #: 000474896500008
ISSN: 1539-1604
Category: A1
Type: Journal Contribution
Appears in Collections: Research publications

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