Extending existing structural identifiability analysis methods to mixed-effects models
Artikel i vetenskaplig tidskrift, 2018

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.

Författare

David Janzén

AstraZeneca AB

Stiftelsen Fraunhofer-Chalmers Centrum för Industrimatematik

The University of Warwick

Mats Jirstrand

Stiftelsen Fraunhofer-Chalmers Centrum för Industrimatematik

Michael Chappell

The University of Warwick

Niel Evans

The University of Warwick

Mathematical Biosciences

0025-5564 (ISSN)

Vol. 295 1-10

Fundament

Grundläggande vetenskaper

Styrkeområden

Livsvetenskaper och teknik (2010-2018)

Ämneskategorier

Bioinformatik och systembiologi

DOI

10.1016/j.mbs.2017.10.009

PubMed

29107004

Mer information

Senast uppdaterat

2020-04-14