title: A two-step iterative algorithm for estimation in nonlinear mixed-effect models with an evaluation in population pharmacokinetics.
abstract: 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.
authors.affiliation: INSERM U194, Service de Biostatistique et Informatique Médicale, CHU Pitié-Salpêtrière, Paris, France.
authors.forename: F
authors.initials: F
authors.lastname: Mentré
authors.forename: R
authors.initials: R
authors.lastname: Gomeni
mesh_terms: D000465:Algorithms; D006801:Humans; D008954:Models, Biological; D004364:Pharmaceutical Preparations / Q000008:administration & dosage; D010599:Pharmacokinetics; D011153:Population; D013810:Therapeutic Equivalency
chemical_list: D004364:Pharmaceutical Preparations