Extending existing structural identifiability analysis methods to mixed-effects models

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Abstract

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. [Abstract copyright: Copyright © 2017 Elsevier Inc. All rights reserved.]

Item Type: Journal Article
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Science, Engineering and Medicine > Engineering > Engineering
SWORD Depositor: Library Publications Router
Library of Congress Subject Headings (LCSH): Multilevel models (Statistics) , Series, Taylor's
Journal or Publication Title: Mathematical Biosciences
Publisher: Elsevier Science Inc.
ISSN: 0025-5564
Official Date: 26 October 2017
Dates:
Date
Event
26 October 2017
Published
20 October 2017
Accepted
Volume: 295
Page Range: pp. 1-10
DOI: 10.1016/j.mbs.2017.10.009
Status: Peer Reviewed
Publication Status: Published
Re-use Statement: ** From PubMed via Jisc Publications Router. ** History: received 04-04-2017; revised 04-08-2017; accepted 20-10-2017.
Access rights to Published version: Restricted or Subscription Access
Date of first compliant deposit: 23 January 2018
Date of first compliant Open Access: 26 October 2018
RIOXX Funder/Project Grant:
Project/Grant ID
RIOXX Funder Name
Funder ID
316736
FP7 People: Marie-Curie Actions
URI: https://wrap.warwick.ac.uk/94847/

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