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2021-06-04Zeitschriftenartikel DOI: 10.1002/psp4.12614
Nonparametric goodness‐of‐fit testing for parametric covariate models in pharmacometric analyses
dc.contributor.authorHartung, Niklas
dc.contributor.authorWahl, Martin
dc.contributor.authorRastogi, Abhishake
dc.contributor.authorHuisinga, Wilhelm
dc.date.accessioned2021-10-18T12:53:37Z
dc.date.available2021-10-18T12:53:37Z
dc.date.issued2021-06-04none
dc.date.updated2021-09-08T09:55:53Z
dc.identifier.urihttp://edoc.hu-berlin.de/18452/24229
dc.description.abstractThe 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.sponsorshipDeutsche Forschungsgemeinschaft (DFG) http://dx.doi.org/10.13039/501100001659
dc.language.isoengnone
dc.publisherHumboldt-Universität zu Berlin
dc.rights(CC BY-NC 4.0) Attribution-NonCommercial 4.0 Internationalger
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.subject.ddc610 Medizin und Gesundheitnone
dc.titleNonparametric goodness‐of‐fit testing for parametric covariate models in pharmacometric analysesnone
dc.typearticle
dc.identifier.urnurn:nbn:de:kobv:11-110-18452/24229-9
dc.identifier.doi10.1002/psp4.12614none
dc.identifier.doihttp://dx.doi.org/10.18452/23572
dc.type.versionpublishedVersionnone
local.edoc.container-titleCPT: pharmacometrics & systems pharmacologynone
local.edoc.pages13none
local.edoc.type-nameZeitschriftenartikel
local.edoc.institutionMathematisch-Naturwissenschaftliche Fakultätnone
local.edoc.container-typeperiodical
local.edoc.container-type-nameZeitschrift
local.edoc.container-publisher-nameNature Publ. Groupnone
local.edoc.container-publisher-placeLondonnone
local.edoc.container-volume10none
local.edoc.container-issue6none
local.edoc.container-firstpage564none
local.edoc.container-lastpage576none
dc.description.versionPeer Reviewednone
dc.identifier.eissn2163-8306
local.edoc.affiliationHartung, Niklas; 1Institute of Mathematics Universität Potsdam Potsdam Germanynone
local.edoc.affiliationWahl, Martin; 2Institute of Mathematics Humboldt‐Universität zu Berlin Berlin Germanynone
local.edoc.affiliationRastogi, Abhishake; 1Institute of Mathematics Universität Potsdam Potsdam Germanynone

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