Exact Gradients Improve Parameter Estimation in Nonlinear Mixed Effects Models with Stochastic Dynamics
Conference poster, 2017

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.


Helga Kristin Olafsdottir

University of Gothenburg

Chalmers, Mathematical Sciences

Jacob Leander

University of Gothenburg

Chalmers, Mathematical Sciences

Joachim Almquist

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Mats Jirstrand

Fraunhofer-Chalmers Centre

American conference on pharmacometrics 2017 (ACoP8)
Fort Lauderdale, ,

Areas of Advance

Information and Communication Technology

Life Science Engineering (2010-2018)

Subject Categories

Computational Mathematics

Probability Theory and Statistics


Basic sciences



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