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.
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Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Bayesian sequential integration within a preclinical pharmacokinetic and pharmacodynamic modeling framework
T2 - Lessons learned
AU - La Gamba, F.
AU - Jacobs, T.
AU - Geys, H.
AU - Jaki, T.
AU - Serroyen, J.
AU - Ursino, M.
AU - Russu, A.
AU - Faes, C.
N1 - 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.
PY - 2019/7/1
Y1 - 2019/7/1
N2 - 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.
AB - 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.
KW - Bayesian inference
KW - nonlinear hierarchical models
KW - pharmacodynamics
KW - pharmacokinetics
KW - recursive
KW - sequential
KW - article
KW - experimental design
KW - preclinical study
U2 - 10.1002/pst.1941
DO - 10.1002/pst.1941
M3 - Journal article
VL - 18
SP - 486
EP - 506
JO - Pharmaceutical Statistics
JF - Pharmaceutical Statistics
SN - 1539-1604
IS - 4
ER -