Performance and Robustness of the Monte Carlo Importance Sampling Algorithm Using Parallelized S-ADAPT for Basic and Complex Mechanistic Models

The Monte Carlo Parametric Expectation Maximization (MC-PEM) algorithm can approximate the true log-likelihood as precisely as needed and is efficiently parallelizable. Our objectives were to evaluate an importance sampling version of the MC-PEM algorithm for mechanistic models and to qualify the de...

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Bibliographic Details
Published in:	The AAPS journal Vol. 13; no. 2; pp. 212 - 226
Main Authors:	Bulitta, Jurgen B., Landersdorfer, Cornelia B.
Format:	Journal Article
Language:	English
Published:	Boston Springer US 01.06.2011
Subjects:	Algorithms Biochemistry Biomedical and Life Sciences Biomedicine Biotechnology Computer Simulation Models, Biological Monte Carlo Method Pharmaceutical Preparations - administration & dosage Pharmaceutical Preparations - metabolism Pharmacokinetics Pharmacology/Toxicology Pharmacy Research Article mechanistic population pharmacokinetic Monte Carlo importance sampling nonlinear mixed-effects modeling in S-ADAPT pharmacodynamic modeling parallel computing Monte Carlo parametric expectation maximization (MC-PEM) algorithm
ISSN:	1550-7416, 1550-7416
Online Access:	Get full text
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