Nonparametric goodness-of-fit testing for parametric covariate models in pharmacometric analyses
- The characterization of covariate effects on model parameters is a crucial step during pharmacokinetic/pharmacodynamic analyses. Although covariate selection criteria have been studied extensively, the choice of the functional relationship between covariates and parameters, however, has received much less attention. Often, a simple particular class of covariate-to-parameter relationships (linear, exponential, etc.) is chosen ad hoc or based on domain knowledge, and a statistical evaluation is limited to the comparison of a small number of such classes. Goodness-of-fit testing against a nonparametric alternative provides a more rigorous approach to covariate model evaluation, but no such test has been proposed so far. In this manuscript, we derive and evaluate nonparametric goodness-of-fit tests for parametric covariate models, the null hypothesis, against a kernelized Tikhonov regularized alternative, transferring concepts from statistical learning to the pharmacological setting. The approach is evaluated in a simulation study on theThe characterization of covariate effects on model parameters is a crucial step during pharmacokinetic/pharmacodynamic analyses. Although covariate selection criteria have been studied extensively, the choice of the functional relationship between covariates and parameters, however, has received much less attention. Often, a simple particular class of covariate-to-parameter relationships (linear, exponential, etc.) is chosen ad hoc or based on domain knowledge, and a statistical evaluation is limited to the comparison of a small number of such classes. Goodness-of-fit testing against a nonparametric alternative provides a more rigorous approach to covariate model evaluation, but no such test has been proposed so far. In this manuscript, we derive and evaluate nonparametric goodness-of-fit tests for parametric covariate models, the null hypothesis, against a kernelized Tikhonov regularized alternative, transferring concepts from statistical learning to the pharmacological setting. The approach is evaluated in a simulation study on the estimation of the age-dependent maturation effect on the clearance of a monoclonal antibody. Scenarios of varying data sparsity and residual error are considered. The goodness-of-fit test correctly identified misspecified parametric models with high power for relevant scenarios. The case study provides proof-of-concept of the feasibility of the proposed approach, which is envisioned to be beneficial for applications that lack well-founded covariate models.…
Author details: | Niklas HartungORCiD, Martin WahlGND, Abhishake RastogiORCiD, Wilhelm HuisingaORCiDGND |
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DOI: | https://doi.org/10.1002/psp4.12614 |
ISSN: | 2163-8306 |
Pubmed ID: | https://pubmed.ncbi.nlm.nih.gov/33755347 |
Title of parent work (English): | CPT: pharmacometrics & systems pharmacology |
Publisher: | Nature Publ. Group |
Place of publishing: | London |
Publication type: | Article |
Language: | English |
Date of first publication: | 2021/03/23 |
Publication year: | 2021 |
Release date: | 2024/01/10 |
Volume: | 10 |
Issue: | 6 |
Number of pages: | 13 |
First page: | 564 |
Last Page: | 576 |
Funding institution: | Deutsche Forschungsgemeinschaft (DFG) German Research Foundation (DFG) [SFB1294/1 - 318763901] |
Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Mathematik |
Fakultät für Gesundheitswissenschaften | |
DDC classification: | 6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit |
Peer review: | Referiert |
Publishing method: | Open Access / Gold Open-Access |
DOAJ gelistet | |
License (German): | CC-BY-NC-ND - Namensnennung, nicht kommerziell, keine Bearbeitungen 4.0 International |