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

La Gamba, F. and Jacobs, T. and Geys, H. and Jaki, T. and Serroyen, J. and Ursino, M. and Russu, A. and Faes, C. (2019) Bayesian sequential integration within a preclinical pharmacokinetic and pharmacodynamic modeling framework:Lessons learned. Pharmaceutical Statistics, 18 (4). pp. 486-506. ISSN 1539-1604

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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.

Item Type:
Journal Article
Journal or Publication Title:
Pharmaceutical Statistics
Additional Information:
This is the peer reviewed version of the following article: La Gamba, F, Jacobs, T, Geys, H, et al. Bayesian sequential integration within a preclinical pharmacokinetic and pharmacodynamic modeling framework: Lessons learned. Pharmaceutical Statistics. 2019; https://doi.org/10.1002/pst.1941 which has been published in final form at https://onlinelibrary.wiley.com/doi/full/10.1002/pst.1941 This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2700/2736
Subjects:
ID Code:
132759
Deposited By:
Deposited On:
26 Apr 2019 09:55
Refereed?:
Yes
Published?:
Published
Last Modified:
20 Sep 2020 05:29