title: Hyperparameter estimation using stochastic approximation with application to population pharmacokinetics.
abstract: A stochastic approximation algorithm is proposed for recursive estimation of the hyperparameters characterizing, in a population, the probability density function of the parameters of a statistical model. For a given population model defined by a parametric model of a biological process, an error model, and a class of densities on the set of the individual parameters, this algorithm provides a sequence of estimates from a sequence of individuals' observation vectors. Convergence conditions are verified for a class of population models including usual pharmacokinetic applications. This method is implemented for estimation of pharmacokinetic population parameters from drug multiple-dosing data. Its estimation capabilities are evaluated and compared to a classical method in population pharmacokinetics, the first-order method (NONMEM), on simulated data.
authors.affiliation: INSERM U194, Département de Biomathématiques, Paris, France.
authors.forename: F
authors.initials: F
authors.lastname: Mentré
authors.forename: A
authors.initials: A
authors.lastname: Mallet
authors.forename: J L
authors.initials: JL
authors.lastname: Steimer
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D008954:Models, Biological; D015233:Models, Statistical; D010599:Pharmacokinetics; D013269:Stochastic Processes