Hyperparameter estimation using stochastic approximation with application to population pharmacokinetics.

F Mentré; A Mallet; J L Steimer

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PubMed · 3203126

Hyperparameter estimation using stochastic approximation with application to population pharmacokinetics.

F Mentré·A Mallet·J L Steimer

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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.

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Algorithms Computer Simulation Models, Biological Models, Statistical Pharmacokinetics Stochastic Processes Journal Article Research Support, Non-U.S. Gov't

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F Mentré, A Mallet, J L Steimer. 1988. Hyperparameter estimation using stochastic approximation with application to population pharmacokinetics.. https://pubmed.ncbi.nlm.nih.gov/3203126/

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