What do we mean by identifiability in mixed effects models?

We discuss the question of model identifiability within the context of nonlinear mixed effects models. Although there has been extensive research in the area of fixed effects models, much less attention has been paid to random effects models. In this context we distinguish between theoretical identi...

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Bibliographic Details
Published in:Journal of pharmacokinetics and pharmacodynamics 2016-02, Vol.43 (1), p.111-122
Main Authors: Lavielle, Marc, Aarons, Leon
Format: Article
Language:English
Subjects:
Algorithms
Biochemistry
Biomedical and Life Sciences
Biomedical Engineering and Bioengineering
Biomedicine
Computer Simulation
Humans
Likelihood Functions
Models, Statistical
Original Paper
Pharmacokinetics
Pharmacology/Toxicology
Pharmacy
Population
Veterinary Medicine/Veterinary Science
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Summary:We discuss the question of model identifiability within the context of nonlinear mixed effects models. Although there has been extensive research in the area of fixed effects models, much less attention has been paid to random effects models. In this context we distinguish between theoretical identifiability, in which different parameter values lead to non-identical probability distributions, structural identifiability which concerns the algebraic properties of the structural model, and practical identifiability, whereby the model may be theoretically identifiable but the design of the experiment may make parameter estimation difficult and imprecise. We explore a number of pharmacokinetic models which are known to be non-identifiable at an individual level but can become identifiable at the population level if a number of specific assumptions on the probabilistic model hold. Essentially if the probabilistic models are different, even though the structural models are non-identifiable, then they will lead to different likelihoods. The findings are supported through simulations.
ISSN:1567-567X
1573-8744
DOI:10.1007/s10928-015-9459-4