Using Fisher Information Matrix to predict uncertainty in covariate effects and power to detect their relevance in Non-Linear Mixed Effect Models in pharmacometrics

• Published in: Journal of pharmacokinetics and pharmacodynamics Vol. 52; no. 4; p. 38
• Main Authors: Fayette, Lucie, Brendel, Karl, Mentré, France
• Format: Journal Article
• Language: English
• Published: New York Springer US 14.07.2025; Springer Nature B.V
• Subjects: Approximation; Biochemistry; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Clinical trials; Computer Simulation; Drug dosages; Humans; Methods; Models, Biological; Models, Statistical; Nonlinear Dynamics; Original Paper; Pharmaceutical industry; Pharmacodynamics; Pharmacokinetics; Pharmacology/Toxicology; Pharmacy; Simulation; Software; Statistical analysis; Uncertainty; Veterinary Medicine/Veterinary Science; Design; Pharmacometrics; Covariate effect; Non-Linear mixed effects models; Power of relevance; Fisher information matrix

This work focuses on design of experiments for Pharmacokinetic (PK) and Pharmacodynamic (PD) studies. Non-Linear Mixed Effects Models (NLMEM) modelling allows the identification and quantification of covariates that explain inter-individual variability (IIV). The Fisher Information Matrix (FIM), com...