Influence of covariance between random effects in design for nonlinear mixed-effect models with an illustration in pediatric pharmacokinetics.
Nonlinear mixed-effect models are used increasingly during drug development. For design, an alternative to simulations is based on the Fisher information matrix. Its expression was derived using a first-order approach, was then extended to include covariance and implemented into the R function PFIM. The impact of covariance on standard errors, amount of information, and optimal designs was studied. It was also shown how standard errors can be predicted analytically within the framework of rich individual data without the model. The results were illustrated by applying this extension to the design of a pharmacokinetic study of a drug in pediatric development.
a Université Paris Diderot, Sorbonne Paris Cité , UMR 738, INSERM, Paris , France.
Cyrielle
C
Dumont
Marylore
M
Chenel
France
F
Mentré
D000704:Analysis of Variance; D015982:Bias; D002648:Child; D003198:Computer Simulation; D004305:Dose-Response Relationship, Drug; D055808:Drug Discovery / Q000379:methods / Q000706:statistics & numerical data; D006801:Humans; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D010372:Pediatrics; D004364:Pharmaceutical Preparations / Q000008:administration & dosage / Q000097:blood / Q000378:metabolism; D010599:Pharmacokinetics; D012107:Research Design
D004364:Pharmaceutical Preparations
D-optimality; Fisher information matrix; Nonlinear mixed effect models; Optimal design; PFIM; Pediatric studies; Population pharmacokinetics