NDLI: Extending existing structural identifiability analysis methods to mixed-effects models

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Extending existing structural identifiability analysis methods to mixed-effects models

Content Provider	Scilit
Author	Janzén, David L. I. Jirstrand, Mats Chappell, Michael J. Evans, Neil D.
Copyright Year	2018
Description	Journal: Mathematical Biosciences The 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 turn determine the distribution of the system's observation function(s). By considering the uniqueness of the analytical statistical moments of the derived functions of the random variables, the structural identifiability of the corresponding mixed-effects model can be determined. The two methods are applied to a set of examples of mixed-effects models to illustrate how they work in practice.
Related Links	http://wrap.warwick.ac.uk/94847/1/WRAP-extending-existing-structural-analysis-models-Evans-2018.pdf
ISSN	00255564
e-ISSN	18793134
DOI	10.1016/j.mbs.2017.10.009
Journal	Mathematical Biosciences
Volume Number	295
Language	English
Publisher	Elsevier BV
Publisher Date	2018-01-01
Access Restriction	Open
Subject Keyword	Journal: Mathematical Biosciences Mathematical Social Sciences Input-output Form Approach Mixed-effects Modelling Structural Identifiability Taylor Series Expansion Approach
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Statistics and Probability Immunology and Microbiology Medicine Biochemistry, Genetics and Molecular Biology Agricultural and Biological Sciences Modeling and Simulation

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