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Author: Zhang, Jun
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Zhang, Jun |
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A Continuous Latent Factor Model for Non-ignorable Missing Data in Longitudinal Studies Ph.D. Dissertation, Arizona State University, 2013 Cohort(s): Children of the NLSY79 Publisher: ProQuest Dissertations & Theses (PQDT) Keyword(s): Data Quality/Consistency; Missing Data/Imputation; Peabody Picture Vocabulary Test (PPVT); Test Scores/Test theory/IRT Permission to reprint the abstract has not been received from the publisher. In this thesis, two studies are presented. The first study is motivated by an open problem from pattern mixture models. Simulation studies from this part show that information in the missing data indicators can be well summarized by a simple continuous latent structure, indicating that a large number of missing data patterns may be accounted by a simple latent factor. Simulation findings that are obtained in the first study lead to a novel model, a continuous latent factor model (CLFM). The second study develops CLFM which is utilized for modeling the joint distribution of missing values and longitudinal outcomes. The proposed CLFM model is feasible even for small sample size applications. The detailed estimation theory, including estimating techniques from both frequentist and Bayesian perspectives is presented. Model performance and evaluation are studied through designed simulations and three applications. Simulation and application settings change from correctly-specified missing data mechanism to mis-specified mechanism and include different sample sizes from longitudinal studies. Among three applications, an AIDS study includes non-ignorable missing values; the Peabody Picture Vocabulary Test data have no indication on missing data mechanism and it will be applied to a sensitivity analysis; the Growth of Language and Early Literacy Skills in Preschoolers with Developmental Speech and Language Impairment study, however, has full complete data and will be used to conduct a robust analysis. The CLFM model is shown to provide more precise estimators, specifically on intercept and slope related parameters, compared with Roy's latent class model and the classic linear mixed model. This advantage will be more obvious when a small sample size is the case, where Roy's model experiences challenges on estimation convergence. The proposed CLFM model is also robust when missing data are ignorable as demonstrated through a study on Growth of Language and Early Literacy Skills in Preschoolers. |
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Bibliography Citation
Zhang, Jun. A Continuous Latent Factor Model for Non-ignorable Missing Data in Longitudinal Studies. Ph.D. Dissertation, Arizona State University, 2013. |