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Assessing robustness of designs for random effects parameters for nonlinear mixed-effects models
Univ Otago, Sch Pharm, 18 Frederick St, Dunedin, New Zealand..
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.ORCID iD: 0000-0002-2676-5912
2017 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 44, no 6, p. 611-616Article in journal (Refereed) Published
Abstract [en]

Optimal designs for nonlinear models are dependent on the choice of parameter values. Various methods have been proposed to provide designs that are robust to uncertainty in the prior choice of parameter values. These methods are generally based on estimating the expectation of the determinant (or a transformation of the determinant) of the information matrix over the prior distribution of the parameter values. For high dimensional models this can be computationally challenging. For nonlinear mixed-effects models the question arises as to the importance of accounting for uncertainty in the prior value of the variances of the random effects parameters. In this work we explore the influence of the variance of the random effects parameters on the optimal design. We find that the method for approximating the expectation and variance of the likelihood is of potential importance for considering the influence of random effects. The most common approximation to the likelihood, based on a first-order Taylor series approximation, yields designs that are relatively insensitive to the prior value of the variance of the random effects parameters and under these conditions it appears to be sufficient to consider uncertainty on the fixed-effects parameters only.

Place, publisher, year, edition, pages
2017. Vol. 44, no 6, p. 611-616
Keywords [en]
Nonlinear mixed-effects models, Optimal design, Random effects, Robust designs
National Category
Pharmacology and Toxicology
Identifiers
URN: urn:nbn:se:uu:diva-342206DOI: 10.1007/s10928-017-9552-yISI: 000415375800008PubMedID: 29064062OAI: oai:DiVA.org:uu-342206DiVA, id: diva2:1184003
Available from: 2018-02-20 Created: 2018-02-20 Last updated: 2018-02-20Bibliographically approved

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Hooker, Andrew

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