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dc.contributor.advisorBell, Melanie
dc.contributor.authorVicenti, Anthony
dc.creatorVicenti, Anthony
dc.date.accessioned2021-02-20T02:34:01Z
dc.date.available2021-02-20T02:34:01Z
dc.date.issued2020
dc.identifier.citationVicenti, Anthony. (2020). Comparison of Mixed Models and Paired T-Test for Analyzing Crossover Clinical Trials in the Presence of Missing Data (Master's thesis, University of Arizona, Tucson, USA).
dc.identifier.urihttp://hdl.handle.net/10150/656850
dc.description.abstractAB/BA crossover clinical trials are popular designs that can achieve high power with a lower number of subjects than other randomized control trial designs. They are often analyzed using paired t-test or mixed models, and like many clinical trials, are often impacted by missing data. Mixed models have been shown to produced more powerful and unbiased results in the presence of missing data than t-tests for other designs, but these two approaches have not been compared in crossover trials. We conducted a simulation study to compare the bias and power of paired t-tests and mixed models when analyzing an AB/BA crossover clinical trial in the presence of missing data. Several different missing structures were simulated under two within-subject correlations, ρ =0.3 and ρ =0.7. Both methods performed similarly when analyzing complete data, but the mixed model produced both equal or less bias estimates and higher power than the paired t-test under all simulation scenarios. In the worst-case scenario we considered, the t-tests resulted in percent bias up to -105% and power as low as 5% compared the mixed model’s percent bias of 1% and 57% power. In less severe cases, both methods had 0% bias, but mixed models still achieved an absolute power gain of 2%-6%. In the presence of missing data, the mixed model achieved higher power than the paired t-test under all simulated scenarios. The mixed model also achieved equal or less bias under all simulated scenarios. Therefore, mixed models should be used over paired t-test when analyzing AB/BA crossover clinical trial in the face of missing data.
dc.language.isoen
dc.publisherThe University of Arizona.
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction, presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
dc.subjectCROSSOVER CLINICAL TRIAL
dc.subjectMissing Data
dc.subjectMixed Model
dc.titleComparison of Mixed Models and Paired T-Test for Analyzing Crossover Clinical Trials in the Presence of Missing Data
dc.typetext
dc.typeElectronic Thesis
thesis.degree.grantorUniversity of Arizona
thesis.degree.levelmasters
dc.contributor.committeememberWatkins, Joe
dc.contributor.committeememberZhou, Jin
thesis.degree.disciplineGraduate College
thesis.degree.disciplineStatistics
thesis.degree.nameM.S.
refterms.dateFOA2021-02-20T02:34:01Z


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