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The longitudinal data analysis plays an important role in a lot of applications today. It is defined by many measurements are obtained over many times. These measurements has complicated correlation structure because they are obtained from the same subjects over the time. In multivariate longitudinal data, there is an additional source of correlation which is "outcomes", the data are obtained over the time for many outcomes for the same subjects. This application could happens in many medical, financial and psychological studies. For example, the patients measurements for some variables are measured over some occasions in order to study the mean changes of these patients. How we can generate and analyze this type of data for complete and incomplete cases is the main goal of this dissertation. It consists of three main studies about the analysis of multivariate binary longitudinal data. The first study is a method to generate correlated binary data for a multivariate longitudinal model with specified correlation structure. This specified structure allows the correlation to be induced over the outcomes or occasions. Second study is a comparison of three methods for analyzing multivariate binary longitudinal data; each one can be beneficial for determined aims. Also, we investigated the difference among the parameter estimations of the three methods. The third study is an investigation of missing data analysis via GEE models, controlling the correlation over the occasions and outcomes via simulation study. However, several methods for handling missing data are used to reduce the bias of the parameter estimations for the incomplete data. these three studies are presented in separated chapters of this dissertation.
A Dissertation submitted to the Department of Statistics in partial fulfillment of the requirements for the degree of Doctor of Philosophy.
Includes bibliographical references.
Elizabeth H. Slate, Professor Directing Dissertation; Amy M. Wetherby, University Representative; Daniel L. McGee, Committee Member; Debajyoti Sinha, Committee Member.
Florida State University
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