Extending existing structural identifiability analysis methods to mixed-effects models

摘要

Highlights?Mixed-effects models are fundamental in drug discovery and development.?Novel analytical methods to study structural identifiability in mixed-effects models.?These methods increase confidence in parameter estimates for mixed-effects models.?Application on example models using the presented methods is provided.AbstractThe concept of structural identifiability for state-space models is expanded to cover mixed-effects state-space models. Two methods applicable for the analytical study of the structural identifiability of mixed-effects models are presented. The two methods are based on previously established techniques for non-mixed-effects models; namely the Taylor series expansion and the input-output form approach. By generating an exhaustive summary, and by assuming an infinite number of subjects, functions of random variables can be derived which in