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A two-step iterative algorithm for estimation in nonlinear mixed-effect models with an evaluation in population pharmacokinetics.

F Mentré·R Gomeni
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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.

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AlgorithmsHumansModels, BiologicalPharmaceutical PreparationsPharmacokineticsPopulationTherapeutic EquivalencyJournal Article

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BibTeXRIS

F Mentré, R Gomeni. 1995. A two-step iterative algorithm for estimation in nonlinear mixed-effect models with an evaluation in population pharmacokinetics.. https://doi.org/10.1080/10543409508835104

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