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dc.contributor.authorDette, Holgerde
dc.date.accessioned2004-12-06T18:39:58Z-
dc.date.available2004-12-06T18:39:58Z-
dc.date.issued2003de
dc.identifier.urihttp://hdl.handle.net/2003/4934-
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-6727-
dc.description.abstractWe consider the design problem for the estimation of several scalar measures suggested in the epidemiological literature for comparing the success rate in two samples. The designs considered so far in the literature are local in the sense that they depend on the unknown probabilities of success in the two groups and are not necessarily robust with respect to their misspecification. A maximin approach is proposed to obtain efficient and robust designs for the estimation of the relative risk, attributable risk and odds ratio, whenever a range for the success rates can be specified by the experimenter. It is demonstrated that the designs obtained by this method are usually more efficient than the uniform design, which allocates equal sample sizes to the two groups.en
dc.format.extent165041 bytes-
dc.format.extent410567 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypeapplication/postscript-
dc.language.isoende
dc.publisherUniversitätsbibliothek Dortmundde
dc.subjecttwo by two tableen
dc.subjectodds ratioen
dc.subjectrelative risken
dc.subjectattributable risken
dc.subjectoptimal designen
dc.subjectefficient designen
dc.subject.ddc310de
dc.titleOn Robust and Efficient Designs for Risk Estimation in Epidemiologic Studiesen
dc.typeTextde
dc.type.publicationtypereporten
dcterms.accessRightsopen access-
Appears in Collections:Sonderforschungsbereich (SFB) 475

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