Parameter estimation in nonlinear mixed effect models based on ordinary differential equations: an optimal control approach

TL;DR

This paper introduces a novel optimal control-based method for parameter estimation in nonlinear mixed effect models with ODEs, effectively handling model misspecification, unknown initial conditions, and identifiability issues, demonstrated through simulations and Ebola vaccine data.

Contribution

It proposes an optimal control approach for NLME-ODEs that improves estimation accuracy and regularizes the problem under challenging conditions like model misspecification and unknown initial states.

Findings

Improved parameter estimation accuracy over maximum likelihood in simulations.

Effectively handles unknown initial conditions and poorly identifiable parameters.

Successfully applied to Ebola vaccine data revealing model discrepancies.

Abstract

We present a parameter estimation method for nonlinear mixed effect models based on ordinary differential equations (NLME-ODEs). The method presented here aims at regularizing the estimation problem in presence of model misspecifications, practical identifiability issues and unknown initial conditions. For doing so, we define our estimator as the minimizer of a cost function which incorporates a possible gap between the assumed model at the population level and the specific individual dynamic. The cost function computation leads to formulate and solve optimal control problems at the subject level. This control theory approach allows to bypass the need to know or estimate initial conditions for each subject and it regularizes the estimation problem in presence of poorly identifiable parameters. Comparing to maximum likelihood, we show on simulation examples that our method improves estimation accuracy in possibly partially observed systems with unknown initial conditions or poorly identifiable parameters with or without model error. We conclude this work with a real application on antibody concentration data after vaccination against Ebola virus coming from phase 1 trials. We use the estimated model discrepancy at the subject level to analyze the presence of model misspecification.