Methods for Analyzing Longitudinal Binary Data with Missing Responses
Public Deposited- Resource Type
- Creator
- Abstract
In this thesis, we explore different estimation methods for longitudinal data with binary responses and drop-outs. We also study the effect of incorrectly specifying the dependence structure or the drop-out mechanism. Although highly efficient, the traditional maximum likelihood (ML) method becomes complex when the number of responses increases, requiring intensive computation. Alternative methods such as generalized estimating equations (GEE) and weighted GEE had been proposed in the literature to overcome the limitation of the ML method. However, both estimators are known to be biased under non-ignorable drop-out mechanisms. The bivariate maximum pseudo likelihood is a pseudo likelihood method that takes into account the correlation between the current and baseline responses. Originally developed for non-monotone missing data, it was modified to be adapted for monotone drop-outs. We conduct a simlation study to assess the sensitivity of each method to model misspecifications in which a non-ignorable drop-out mechanism is our primary interest.
- Subject
- Language
- Publisher
- Thesis Degree Level
- Thesis Degree Name
- Thesis Degree Discipline
- Identifier
- Rights Notes
Copyright © 2015 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
- Date Created
- 2015
Relations
- In Collection:
Items
Thumbnail | Title | Date Uploaded | Visibility | Actions |
---|---|---|---|---|
kim-methodsforanalyzinglongitudinalbinarydata.pdf | 2023-05-04 | Public | Download |