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.
Author
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, ,
Fort Lauderdale, ,
Areas of Advance
Information and Communication Technology
Life Science Engineering (2010-2018)
Subject Categories
Computational Mathematics
Probability Theory and Statistics
Roots
Basic sciences
DOI
10.1007/s10928-017-9536-y