Modeling Methods
Under construction
Important considerations
Impact of a model [1,2]
- What is the modeling used for? (e.g., bridging, dose, SmPC1 parameters?)
- Does the conclusion align with the aim?
- What data is available?
- Rich data
- Sparse data
- What is the structural model?
- Exposure-response is generally non-informative if only one dose-level is given, even if weight-adjusted
Reviewing models
- Does my conclusion align with the authors?
- Questions NGN (eNGiNe)
- Need-to-know: Will affect conclusion (Major objection)
- Good-to-know: Could affect conclusion (Other concern)
- Nice-to-know: Won’t affect conclusion (avoid asking this question)
Terminology
Parsimony
The idea that comparing two models, the model with fewer parameters is preferrable, given that all else is equal.
Shrinkage
A metric which quantifies how much individual estimates regress towards the population mean under the given sampling schedule [3].
References
[1]
Musuamba FT, Skottheim Rusten I, Lesage R, Russo G, Bursi R, Emili L, et al. Scientific and regulatory evaluation of mechanistic in Silico drug and disease models in drug development: Building model credibility. CPT Pharmacometrics Syst Pharmacol 2021;10:804–25. https://doi.org/10.1002/psp4.12669.
[2]
Skottheim Rusten I, Musuamba FT. Scientific and regulatory evaluation of empirical pharmacometric models: An application of the risk informed credibility assessment framework. CPT Pharmacometrics Syst Pharmacol 2021;10:1281–96. https://doi.org/10.1002/psp4.12708.
[3]
Savic RM, Karlsson MO. Importance of Shrinkage in Empirical Bayes Estimates for Diagnostics: Problems and Solutions. AAPS J 2009;11:558–69. https://doi.org/10.1208/s12248-009-9133-0.
