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dc.contributor.advisorMüller, Peter, 1963 August 9-en
dc.creatorWang, Yisien
dc.date.accessioned2015-11-16T19:53:36Zen
dc.date.available2015-11-16T19:53:36Zen
dc.date.issued2015-05en
dc.date.submittedMay 2015en
dc.identifierdoi:10.15781/T2QW76en
dc.identifier.urihttp://hdl.handle.net/2152/32505en
dc.descriptiontexten
dc.description.abstractIn randomized clinical trials, medical researchers are interested to determine the effectiveness of a new treatment not only in the overall population but also to some subgroups with possible enhanced treatment effects. However, subgroup analysis may become problematic due to the issue of multiplicities, data dredging etc. Accounting for these issue, we summarized some guidelines on the use and interpretation of subgroup analysis. We reviewed three approaches to subgroup analysis, a tranditional Bayesian regression with interaction terms, the 'Virtual Twins' methods and a Bayesian model selection approach. The advantage and disadvantage of these three approaches are discussed.en
dc.format.mimetypeapplication/pdfen
dc.language.isoenen
dc.subjectSubgroup analysisen
dc.titleA comparison of multiple approaches to subgroup analysis in clinical trialen
dc.typeThesisen
dc.date.updated2015-11-16T19:53:36Zen
dc.contributor.committeeMemberDaniels , Michael Jen
dc.description.departmentStatisticsen
thesis.degree.departmentStatisticsen
thesis.degree.disciplineStatisticsen
thesis.degree.grantorThe University of Texas at Austinen
thesis.degree.levelMastersen
thesis.degree.nameMaster of Science in Statisticsen


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