A two-step iterative algorithm for estimation in nonlinear mixed-effect models with an evaluation in population pharmacokinetics.
This article proposes an EM-like algorithm for estimating, by maximum likelihood, the population parameters of a nonlinear mixed-effect model given sparse individual data. The first step involves Bayesian estimation of the individual parameters. During the second step, population parameters are estimated using a linearization about those Bayesian estimates. This algorithm (implemented in P-PHARM) is evaluated on simulated data, mimicking pharmacokinetic analyses and compared to the First-Order method and the First-Order Conditional Estimates method (both implemented in NONMEM). The accuracy of the results, within few iterations, shows the estimation capabilities of the proposed approach.
Mentré
F
F
INSERM U194, Service de Biostatistique et Informatique Médicale, CHU Pitié-Salpêtrière, Paris, France.
Gomeni
R
R
D000465:Algorithms; D006801:Humans; D008954:Models, Biological; D004364:Pharmaceutical Preparations / Q000008:administration & dosage; D010599:Pharmacokinetics; D011153:Population; D013810:Therapeutic Equivalency
D004364:Pharmaceutical Preparations