Nonparametric goodness‐of‐fit testing for parametric covariate models in pharmacometric analyses
dc.contributor.author | Hartung, Niklas | |
dc.contributor.author | Wahl, Martin | |
dc.contributor.author | Rastogi, Abhishake | |
dc.contributor.author | Huisinga, Wilhelm | |
dc.date.accessioned | 2021-10-18T12:53:37Z | |
dc.date.available | 2021-10-18T12:53:37Z | |
dc.date.issued | 2021-06-04 | none |
dc.date.updated | 2021-09-08T09:55:53Z | |
dc.identifier.uri | http://edoc.hu-berlin.de/18452/24229 | |
dc.description.abstract | 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 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. | eng |
dc.description.sponsorship | Deutsche Forschungsgemeinschaft (DFG) http://dx.doi.org/10.13039/501100001659 | |
dc.language.iso | eng | none |
dc.publisher | Humboldt-Universität zu Berlin | |
dc.rights | (CC BY-NC 4.0) Attribution-NonCommercial 4.0 International | ger |
dc.rights.uri | https://creativecommons.org/licenses/by-nc/4.0/ | |
dc.subject.ddc | 610 Medizin und Gesundheit | none |
dc.title | Nonparametric goodness‐of‐fit testing for parametric covariate models in pharmacometric analyses | none |
dc.type | article | |
dc.identifier.urn | urn:nbn:de:kobv:11-110-18452/24229-9 | |
dc.identifier.doi | http://dx.doi.org/10.18452/23572 | |
dc.type.version | publishedVersion | none |
local.edoc.pages | 13 | none |
local.edoc.type-name | Zeitschriftenartikel | |
local.edoc.container-type | periodical | |
local.edoc.container-type-name | Zeitschrift | |
dc.description.version | Peer Reviewed | none |
dc.identifier.eissn | 2163-8306 | |
dcterms.bibliographicCitation.doi | 10.1002/psp4.12614 | none |
dcterms.bibliographicCitation.journaltitle | CPT: pharmacometrics & systems pharmacology | none |
dcterms.bibliographicCitation.volume | 10 | none |
dcterms.bibliographicCitation.issue | 6 | none |
dcterms.bibliographicCitation.originalpublishername | Nature Publ. Group | none |
dcterms.bibliographicCitation.originalpublisherplace | London | none |
dcterms.bibliographicCitation.pagestart | 564 | none |
dcterms.bibliographicCitation.pageend | 576 | none |
bua.import.affiliation | Hartung, Niklas; 1Institute of Mathematics Universität Potsdam Potsdam Germany | none |
bua.import.affiliation | Wahl, Martin; 2Institute of Mathematics Humboldt‐Universität zu Berlin Berlin Germany | none |
bua.import.affiliation | Rastogi, Abhishake; 1Institute of Mathematics Universität Potsdam Potsdam Germany | none |
bua.department | Mathematisch-Naturwissenschaftliche Fakultät | none |