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A Continuous Latent Factor Model for Non-ignorable Missing Data in Longitudinal Studies


Abstract Many longitudinal studies, especially in clinical trials, suffer from missing data issues. Most estimation procedures assume that the missing values are ignorable or missing at random (MAR). However, this assumption leads to unrealistic simplification and is implausible for many cases. For example, an investigator is examining the effect of treatment on depression. Subjects are scheduled with doctors on a regular basis and asked questions about recent emotional situations. Patients who are experiencing severe depression are more likely to miss an appointment and leave the data missing for that particular visit. Data that are not missing at random may produce bias in results if the missing mechanism is not taken into account. In other words,... (more)
Created Date 2013
Contributor Zhang, Jun (Author) / Reiser, Mark (Advisor) / Barber, Jarrett (Advisor) / Kao, Ming-Hung (Committee member) / Wilson, Jeffrey (Committee member) / St Louis, Robert D. (Committee member) / Arizona State University (Publisher)
Subject Statistics
Type Doctoral Dissertation
Extent 153 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note Ph.D. Statistics 2013
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis