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Extending existing structural identifiability analysis methods to mixed-effects models.

David L I Janzén1, Mats Jirstrand2, Michael J Chappell3

  • 1Drug Metabolism and Pharmacokinetics, Cardiovascular and Metabolic Diseases, IMED Biotech Unit, AstraZeneca, Gothenburg, Sweden; Department of Systems and Data Analysis, Fraunhofer-Chalmers Centre, Chalmers Science Park, SE-412 88 Gothenburg, Sweden; School of Engineering, University of Warwick, Coventry, CV4 7AL, United Kingdom.

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|November 7, 2017
PubMed
Summary
This summary is machine-generated.

This study extends structural identifiability analysis to mixed-effects state-space models using Taylor series and input-output methods. It provides a framework for determining model parameter identifiability in complex systems.

Keywords:
Input-Output form approachMixed-Effects modellingStructural identifiabilityTaylor series expansion approach

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Area of Science:

  • Pharmacometrics
  • Systems Biology
  • Mathematical Modeling

Background:

  • Structural identifiability is crucial for parameter estimation in state-space models.
  • Mixed-effects models are widely used in pharmacometrics and systems biology to account for inter-individual variability.
  • Extending identifiability analysis to these complex models is essential for robust model development.

Purpose of the Study:

  • To extend the concept of structural identifiability to mixed-effects state-space models.
  • To present two analytical methods for assessing the structural identifiability of these models.
  • To demonstrate the practical application of these methods through examples.

Main Methods:

  • Adaptation of Taylor series expansion for mixed-effects models.
  • Adaptation of the input-output form approach for mixed-effects models.
  • Derivation of functions of random variables and analysis of their statistical moments assuming an infinite number of subjects.

Main Results:

  • Two novel analytical methods for structural identifiability of mixed-effects state-space models are presented.
  • The methods successfully determine the structural identifiability by analyzing derived functions of random variables.
  • The applicability of the methods is illustrated using various mixed-effects model examples.

Conclusions:

  • The presented methods provide a rigorous framework for assessing structural identifiability in mixed-effects state-space models.
  • These analytical tools are valuable for ensuring reliable parameter estimation in complex biological and pharmacological systems.
  • The study facilitates the development of more robust and interpretable mixed-effects models.