الفهرس 
Title: Fitting linear mixed model for incomplete multivariate longitudinal data
AbstractOnly 14 pages are availabe for public view
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Abstract
Measurements made on several outcomes for the same unit, implying multivariate longitudinal data (MLD), are very likely to be correlated. Therefore, fitting such a data structure can be quite challenging due to the high dimensioned correlations exist within and between outcomes over time. In addition, an essential addendum challenge is encountered in biomedical longitudinal design because of the premature withdrawal of the subjects from the study resulting in incomplete data. Incomplete data is more problematic when missing data mechanism is related to the unobserved outcomes implying what so-called non-ignorable missing data. Obtaining valid estimation under non-ignorable assumption requires that the missing-data mechanism be modeled as a part of the estimation process. The multiple continuous outcomes data model is introduced via the Gaussian multivariate linear mixed models (MLMM) while the missing-data mechanism is linked to the data model via the selection model such that the missing-data mechanism parameters are fitted using the multivariate logistic regression. The current thesis proposed a stochastic expectation-maximization (SEM) algorithm to fit MLD in the presence of non-ignorable dropout