Show simple item record

dc.contributor.supervisor Wang, Xikui (Statistics) en_US
dc.contributor.author LI, XUAN
dc.date.accessioned 2012-06-22T20:04:45Z
dc.date.available 2012-06-22T20:04:45Z
dc.date.issued 2012 en_US
dc.identifier.citation Journal of Statistical Planning and Inference 142 (2012), pp. 2128-2135 en_US
dc.identifier.uri http://hdl.handle.net/1993/8095
dc.description.abstract Response 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.publisher Elsevier en_US
dc.rights info:eu-repo/semantics/openAccess
dc.subject Clinical trials en_US
dc.subject Misclassification en_US
dc.subject Optimal allocation en_US
dc.subject Power en_US
dc.subject Response adaptive designs en_US
dc.subject Variance-penalized criterion en_US
dc.title Response Adaptive Designs in the Presence of Mismeasurement en_US
dc.type info:eu-repo/semantics/doctoralThesis
dc.degree.discipline Statistics en_US
dc.contributor.examiningcommittee Alfa, Attahiru (Electrical and Computer Engineering) Fu, James (Statistics) Johnson, Brad (Statistics) Yi, Grace Y. (University of Waterloo) en_US
dc.degree.level Doctor of Philosophy (Ph.D.) en_US
dc.description.note October 2012 en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

View Statistics