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Nonlinear mixed-effects models for pharmacokinetic data analysis: assessment of the random-effects distribution

Drikvandi, Reza

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Abstract

Nonlinear mixed-effects models are frequently used for pharmacokinetic data analysis, and they account for inter-subject variability in pharmacokinetic parameters by incorporating subject-specific random effects into the model. The random effects are often assumed to follow a (multivariate) normal distribution. However, many articles have shown that misspecifying the random-effects distribution can introduce bias in the estimates of parameters and affect inferences about the random effects themselves, such as estimation of the inter-subject variability. Because random effects are unobservable latent variables, it is difficult to assess their distribution. In a recent paper we developed a diagnostic tool based on the so-called gradient function to assess the random-effects distribution in mixed models. There we evaluated the gradient function for generalized liner mixed models and in the presence of a single random effect. However, assessing the random-effects distribution in nonlinear mixed-effects models is more challenging, especially when multiple random effects are present, and therefore the results from linear and generalized linear mixed models may not be valid for such nonlinear models. In this paper, we further investigate the gradient function and evaluate its performance for such nonlinear mixed-effects models which are common in pharmacokinetics and pharmacodynamics. We use simulations as well as real data from an intensive pharmacokinetic study to illustrate the proposed diagnostic tool.

Citation

Drikvandi, R. (2017). Nonlinear mixed-effects models for pharmacokinetic data analysis: assessment of the random-effects distribution. Journal of Pharmacokinetics and Pharmacodynamics, 44(3), 223-232. https://doi.org/10.1007/s10928-017-9510-8

Journal Article Type Article
Acceptance Date Feb 7, 2017
Online Publication Date Feb 13, 2017
Publication Date 2017-06
Deposit Date Oct 6, 2020
Publicly Available Date Mar 28, 2024
Journal Journal of Pharmacokinetics and Pharmacodynamics
Print ISSN 1567-567X
Electronic ISSN 1573-8744
Publisher Springer
Peer Reviewed Peer Reviewed
Volume 44
Issue 3
Pages 223-232
DOI https://doi.org/10.1007/s10928-017-9510-8

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Copyright Statement
This is a post-peer-review, pre-copyedit version of a journal article published in Journal of pharmacokinetics and pharmacodynamics. The final authenticated version is available online at: https://doi.org/10.1007/s10928-017-9510-8




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