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dc.contributor.advisorSeaman, John Weldon, 1956-
dc.contributor.authorMoore, Page Casey.
dc.contributor.otherBaylor University. Dept. of Statistical Sciences.en
dc.date.accessioned2007-02-07T18:57:26Z
dc.date.available2007-02-07T18:57:26Z
dc.date.copyright2006-08
dc.date.issued2007-02-07T18:57:26Z
dc.identifier.urihttp://hdl.handle.net/2104/4880
dc.descriptionIncludes bibliographical references (p. 190-201).en
dc.description.abstractClinical trial endpoints are traditionally either physical or laboratory responses. However, such endpoints fail to reflect how patients feel or function in their daily activities. Missing data is inevitable in most every clinical trial regardless of the amount of effort and pre-planning that originally went into a study. Many researchers often resort to ad hoc methods(e.g. case-deletion or mean imputation) when they are faced with missing data, which can lead to biased results. An alternative to these ad hoc methods is multiple imputation. Sources of missing data due to patient dropout in health related quality of life (HRQoL) studies most often result from one of the following: toxicity, disease progression, or therapeutic effectiveness. As a result, nonignorable (NMAR) missing data are the most common type of missing data found in HRQoL studies. Studies involving missing data with a NMAR mechanism are the most difficult type of data to analyze primarily for two reasons: a large number of potential models exist for these data and the hypothesis of random dropout can be neither confirmed nor repudiated. The performance of methods used for the analysis of discrete longitudinal clinical trial data considered to have a nonignorable missingness mechanism under the commonly applied restriction of monotone dropout were developed and evaluated in this dissertation. Monotone dropout, or attrition, occurs when responses are available for a patient until a certain occasion and missing for all subsequent occasions. The purpose of this study is to investigate the performance of different imputation methods available to researchers for handling the problem of missing data where the parameters of interest are six QoL assessments scheduled for collection across six equally spaced visits. We evaluate the relative effectiveness of three commonly used imputation methods, along with three restriction methods and a newly developed restriction method, through a simulation study. The new restriction method is a straightforward technique that provides superior overall performance and much higher coverage rates relative to the other methods under investigation.en
dc.description.statementofresponsibilityby Page Casey Moore.en
dc.format.extentxviii, 201 p. : ill.en
dc.format.extent2012436 bytes
dc.format.extent4121020 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen
dc.rightsBaylor University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. Contact librarywebmaster@baylor.edu for inquiries about permission.en
dc.subjectLongitudinal method.en
dc.subjectMultivariate analysis.en
dc.titleA restriction method for the analysis of discrete longitudinal missing data.en
dc.typeThesisen
dc.description.degreePh.D.en
dc.rights.accessrightsBaylor University access onlyen
dc.contributor.departmentStatistical Sciences.en


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