الفهرس 
Title : Joint Modeling of Longitudinal Measurements and Time-to-Event in the Presence of Missing Data
AbstractOnly 14 pages are availabe for public view
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Abstract
Most medical studies produce two di erent types of data for each study subject: longitudinal and time-to-event data. Joint analysis is an appealing approach that can model the association between an event of interest and a time dependent covariate that is measured with error. Although primarily developed for this purpose; joint models can also be used as an e cient tool to handle nonignorable missingness in longitudinal studies. In this thesis, the model proposed by Tseng et al. (2016) to handle both monotone and non-monotone missingness simultaneously is adopted.The Stochastic EM (SEM) algorithm is proposed and developed to obtain the parameter estimates for the joint model. In addition, the Monte Carlo method is developed to compute the standard errors of the estimates.The proposed approach is illustrated using data from a clinical trial for the Scleroderma lung disease in addition to a simulation study