Optimal designs for dose response curves with common parameters

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dc.contributor.authorFeller, Chrystel
dc.contributor.authorSchorning, Kirsten
dc.contributor.authorDette, Holger
dc.contributor.authorBermann, Georgina
dc.contributor.authorBornkamp, Björn
dc.date.accessioned2016-03-15T14:46:31Z
dc.date.available2016-03-15T14:46:31Z
dc.date.issued2016
dc.description.abstractA common problem in Phase II clinical trials is the comparison of dose response curves corresponding to different treatment groups. If the effect of the dose level is described by parametric regression models and the treatments differ in the administration frequency (but not in the sort of drug) a reasonable assumption is that the regression models for the different treatments share common parameters. This paper develops optimal design theory for the comparison of different regression models with common parameters. We derive upper bounds on the number of support points of admissible designs, and explicit expressions for D-optimal designs are derived for frequently used dose response models with a common location parameter. If the location and scale parameter in the different models coincide, minimally supported designs are determined and sufficient conditions for their optimality in the class of all designs derived. The results are illustrated in a dose-finding study comparing monthly and weekly administration.en
dc.identifier.urihttp://hdl.handle.net/2003/34840
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-16892
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB 823;13/2016en
dc.subjectnonlinear regressionen
dc.subjectBayesian optimal designen
dc.subjectadmissible designde
dc.subjectmodels with common parametersen
dc.subjectD-optimal designen
dc.subjectdifferent treatment groupsen
dc.subject.ddc310
dc.subject.ddc330
dc.subject.ddc620
dc.titleOptimal designs for dose response curves with common parametersen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access

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Sonderforschungsbereich (SFB) 823