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dc.contributor.supervisorWang, Xikui (Statistics)en_US
dc.contributor.authorLI, XUAN
dc.date.accessioned2012-06-22T20:04:45Z
dc.date.available2012-06-22T20:04:45Z
dc.date.issued2012en_US
dc.identifier.citationJournal of Statistical Planning and Inference 142 (2012), pp. 2128-2135en_US
dc.identifier.urihttp://hdl.handle.net/1993/8095
dc.description.abstractResponse adaptive randomization represents a major advance in clinical trial methodology that helps balance the benefits of the collective and the benefits of the individual and improves efficiency without undermining the validity and integrity of the clinical research. Response adaptive designs use information so far accumulated from the trial to modify the randomization procedure and deliberately bias treatment allocation in order to assign more patients to the potentially better treatment. No attention has been paid to incorporating the problem of errors-in-variables in adaptive clinical trials. In this work, some important issues and methods of response adaptive design of clinical trials in the presence of mismeasurement are examined. We formulate response adaptive designs when the dichotomous response may be misclassified. We consider the optimal allocations under various objectives, investigate the asymptotically best response adaptive randomization procedure, and discuss effects of misclassification on the optimal allocation. We derive explicit expressions for the variance-penalized criterion with misclassified binary responses and propose a new target proportion of treatment allocation under the criterion. A real-life clinical trial and some related simulation results are also presented.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectClinical trialsen_US
dc.subjectMisclassificationen_US
dc.subjectOptimal allocationen_US
dc.subjectPoweren_US
dc.subjectResponse adaptive designsen_US
dc.subjectVariance-penalized criterionen_US
dc.titleResponse Adaptive Designs in the Presence of Mismeasurementen_US
dc.typeinfo:eu-repo/semantics/doctoralThesis
dc.typedoctoral thesisen_US
dc.degree.disciplineStatisticsen_US
dc.contributor.examiningcommitteeAlfa, Attahiru (Electrical and Computer Engineering) Fu, James (Statistics) Johnson, Brad (Statistics) Yi, Grace Y. (University of Waterloo)en_US
dc.degree.levelDoctor of Philosophy (Ph.D.)en_US
dc.description.noteOctober 2012en_US


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