Home > Research > Publications & Outputs > Bayesian sequential integration within a precli...

Associated organisational unit

Electronic data

  • Final submitted version

    Rights statement: 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.

    Accepted author manuscript, 3.98 MB, PDF document

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

Links

Text available via DOI:

View graph of relations

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

Research output: Contribution to journalJournal article

Published
  • F. La Gamba
  • T. Jacobs
  • H. Geys
  • T. Jaki
  • J. Serroyen
  • M. Ursino
  • A. Russu
  • C. Faes
Close
<mark>Journal publication date</mark>1/07/2019
<mark>Journal</mark>Pharmaceutical Statistics
Issue number4
Volume18
Number of pages21
Pages (from-to)486-506
Publication StatusPublished
Early online date1/04/19
<mark>Original language</mark>English

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.

Bibliographic note

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.