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Exact Gradients Improve Parameter Estimation in Nonlinear Mixed Effects Models with Stochastic Dynamics

Authors Helga Kristin Olafsdottir
Jacob Leander
Joachim Almquist
Mats Jirstrand
Published in Journal of Pharmacokinetics and Pharmacodynamics. 44(Suppl 1): 11
ISSN 1567-567X
Publication year 2017
Published at Department of Mathematical Sciences
Language en
Subject categories Optimization, systems theory, Statistics, Mathematical statistics


Nonlinear mixed effects (NLME) models based on stochastic differential equations (SDEs) have evolved into a mature approach for analysis of PKPD data [1-3], but parameter estimation remains challenging. We present an exact-gradient version of the first order conditional estimation (FOCE) method for SDE-NLME models, and investigate whether it enables faster estimation and better gradient precision/accuracy compared to finite difference gradients.

Page Manager: Webmaster|Last update: 9/11/2012

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