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dc.contributor.advisorSeaman, John Weldon, 1956-
dc.contributor.authorFeng, Chunyao.
dc.contributor.otherBaylor University. Dept. of Statistical Sciences.en
dc.date.accessioned2006-07-29T17:03:06Z
dc.date.available2006-07-29T17:03:06Z
dc.date.copyright2006-05
dc.date.issued2006-07-29T17:03:06Z
dc.identifier.urihttp://hdl.handle.net/2104/4187
dc.descriptionIncludes bibliographical references (p. 115-117).en
dc.description.abstractTo save time and reduce the size and cost of clinical trials, surrogate endpoints are frequently measured instead of true endpoints. The proportion of the treatment effect explained by surrogate endpoints (PTE) is a widely used, albeit controversial, validation criteria. Frequentist and Bayesian methods have been developed to facilitate such validation. The former does not formally incorporate prior information; a critical issue since confidence intervals on PTE is often unacceptably wide. Both the Bayesian and frequentist approaches may yield estimates of PTE outside the unit interval. Furthermore, the existing Bayesian method offers no insight into the prior used for PTE, making prior-to-posterior sensitivity analyses problematic. We proposed a fully Bayesian approach that avoids both of these problems. We also consider the effect of interaction on inference for PTE. As an alternative to the use of PTE, we develop a Bayesian model for relative effect and the association between surrogate and true endpoints, making use of power priors.en
dc.description.statementofresponsibilityby Chunyao Feng.en
dc.format.extentxi, 117 p. : ill.en
dc.format.extent791062 bytes
dc.format.extent135196 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen
dc.rightsBaylor University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. Contact librarywebmaster@baylor.edu for inquiries about permission.en
dc.subjectBayesian statistical decision theory.en
dc.subjectExperimental design.en
dc.titleBayesian evaluation of surrogate endpoints.en
dc.typeThesisen
dc.description.degreePh.D.en
dc.rights.accessrightsWorldwide access.en
dc.rights.accessrightsAccess changed 5/24/11.
dc.contributor.departmentStatistical Sciences.en


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