Bayesian optimal designs for dose-response curves with common parameters

dc.contributor.author	Schorning, Kirsten
dc.contributor.author	Konstantinou, Maria
dc.date.accessioned	2017-11-14T13:23:20Z
dc.date.available	2017-11-14T13:23:20Z
dc.date.issued	2017
dc.description.abstract	The issue of determining not only an adequate dose but also a dosing frequency of a drug arises frequently in Phase II clinical trials. This results in the comparison of models which have some parameters in common. Planning such studies based on Bayesian optimal designs offers robustness to our conclusions since these designs, unlike locally optimal designs, are efficient even if the parameters are misspecified. In this paper we develop approximate design theory for Bayesian D-optimality for nonlinear regression models with common parameters and investigate the cases of common location or common location and scale parameters separately. Analytical characterisations of saturated Bayesian D-optimal designs are derived for frequently used dose-response models and the advantages of our results are illustrated via a numerical investigation.	en
dc.identifier.uri	http://hdl.handle.net/2003/36181
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-18197
dc.language.iso	en	de
dc.relation.ispartofseries	Discussion Paper / SFB823;22/2017	en
dc.subject.ddc	310
dc.subject.ddc	330
dc.subject.ddc	620
dc.title	Bayesian optimal designs for dose-response curves with common parameters	en
dc.type	Text	de
dc.type.publicationtype	workingPaper	de
dcterms.accessRights	open access
eldorado.secondarypublication	false	de

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