Analysis of Repeated Measures in the Presence of Missing Observations due to Dropout

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dc.contributor.advisorSmith, Paul Jen_US
dc.contributor.advisorTsong, Yien_US
dc.contributor.authorLi, Jingen_US
dc.contributor.departmentMathematicsen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2013-06-28T06:10:23Z
dc.date.available2013-06-28T06:10:23Z
dc.date.issued2013en_US
dc.description.abstractIncomplete data is common in both observational studies and clinical trials. Ignoring missing data may produce seriously biased estimators and could lead to misleading results. During the last three decades, a vast amount of work has been done in this area. The approaches can be classified into the following main categories: imputation methods, likelihood-based methods and inverse probability weighting methods. Longitudinal and crossover studies with repeated measures are particularly subject to missing observations. Various methods, including generalized estimating equations (GEE) (Liang and Zeger, 1986), weighted GEE (WGEE) (Robins, Rotnitzky and Zhao, 1995) and multiple imputations, have been proposed to cope with missing data in longitudinal studies. However, very few researchers have explored the missing data issue in crossover studies. In addition to reviewing and critiquing the methods dealing with missing observations in general and in repeated measures, in this dissertation, we propose a new weighting approach for GEE to estimate the regression parameters in crossover studies. The proposed method provides consistent and asymptotically normally distributed estimators. Simulation and asymptotic efficiency results indicate that the proposed estimators are more efficient than both regular GEE and WGEE. Applications of the proposed method are illustrated with real data.en_US
dc.identifier.urihttp://hdl.handle.net/1903/14041
dc.subject.pqcontrolledStatisticsen_US
dc.subject.pqcontrolledBiostatisticsen_US
dc.subject.pquncontrolledCrossover Studyen_US
dc.subject.pquncontrolledLongitudinal Studyen_US
dc.subject.pquncontrolledMissing at Randomen_US
dc.subject.pquncontrolledMissing Dataen_US
dc.subject.pquncontrolledRepeated Measuresen_US
dc.subject.pquncontrolledWeighted Generalized Estiamting Equationen_US
dc.titleAnalysis of Repeated Measures in the Presence of Missing Observations due to Dropouten_US
dc.typeDissertationen_US

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