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
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
| <mark>Journal publication date</mark> | 1/07/2019 |
|---|---|
| <mark>Journal</mark> | Pharmaceutical Statistics |
| Issue number | 4 |
| Volume | 18 |
| Number of pages | 21 |
| Pages (from-to) | 486-506 |
| Publication Status | Published |
| Early online date | 1/04/19 |
| <mark>Original language</mark> | English |
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.