A nonparametric approach to practical identifiability of nonlinear mixed effects models

TL;DR

This paper introduces a nonparametric method for assessing practical identifiability in hierarchical nonlinear mixed effects models, addressing a gap in existing techniques for population-level parameter estimation.

Contribution

It presents a novel nonparametric approach tailored for hierarchical models, extending identifiability analysis beyond individual-based methods.

Findings

Demonstrated the approach on pharmacometrics data

Applied the method to viral dynamics models

Showed improved identifiability assessment in hierarchical settings

Abstract

Mathematical modelling is a widely used approach to understand and interpret clinical trial data. This modelling typically involves fitting mechanistic mathematical models to data from individual trial participants. Despite the widespread adoption of this individual-based fitting, it is becoming increasingly common to take a hierarchical approach to parameter estimation, where modellers characterize the population parameter distributions, rather than considering each individual independently. This hierarchical parameter estimation is standard in pharmacometric modelling. However, many of the existing techniques for parameter identifiability do not immediately translate from the individual-based fitting to the hierarchical setting. Here, we propose a nonparametric approach to study practical identifiability within a hierarchical parameter estimation framework. We focus on the commonly used nonlinear mixed effects framework and investigate two well-studied examples from the pharmacometrics and viral dynamics literature to illustrate the potential utility of our approach.