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A review on estimation of stochastic differential equations for pharmacokinetic/pharmacodynamic models

dc.contributor.authorDonnet, Sophie
HAL ID: 14568
ORCID: 0000-0003-4370-7316
dc.contributor.authorSamson, Adeline
HAL ID: 9408
dc.date.accessioned2013-06-18T07:54:44Z
dc.date.available2013-06-18T07:54:44Z
dc.date.issued2013
dc.identifier.issn0169-409X
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/11429
dc.language.isoenen
dc.subjectPharmacodynamic
dc.subjectStochastic differential equations
dc.subjectPharmacokinetic
dc.subjectKalman Filter
dc.subjectBayesian estimation
dc.subjectEM algorithm
dc.subjectpopulation approach
dc.subjectmaximum likelihood estimation
dc.subjectGauss quadrature
dc.subjectHermite expansion
dc.subject.ddc519en
dc.subject.classificationjelC11en
dc.titleA review on estimation of stochastic differential equations for pharmacokinetic/pharmacodynamic models
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenThis paper is a survey of existing estimation methods for pharmacokinetic/pharmacodynamic (PK/PD) models based on stochastic differential equations (SDEs). Most parametric estimation methods proposed for SDEs require high frequency data and are often poorly suited for PK/PD data which are usually sparse. Moreover, PK/PD experiments generally include not a single individual but a group of subjects, leading to a population estimation approach. This review concentrates on estimation methods which have been applied to PK/PD data, for SDEs observed with and without measurement noise, with a standard or a population approach. Besides, the adopted methodologies highly differ depending on the existence or not of an explicit transition density of the SDE solution.
dc.relation.isversionofjnlnameAdvanced Drug Delivery Reviews
dc.relation.isversionofjnlvol65
dc.relation.isversionofjnlissue7
dc.relation.isversionofjnldate2013
dc.relation.isversionofjnlpages929-939
dc.relation.isversionofdoihttp://dx.doi.org/10.1016/j.addr.2013.03.005
dc.relation.isversionofjnlpublisherElsevier
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.description.ssrncandidatenon
dc.description.halcandidatenon
dc.description.readershiprecherche
dc.description.audienceInternational
dc.relation.Isversionofjnlpeerreviewedoui
dc.date.updated2017-03-13T16:18:53Z


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