Analyzing Incomplete Longitudinal Binary Data Using Approximate Likelihood Methods

Nonignorable missingness in the outcome variable complicates the longitudinal data analysis, as it is necessary to incorporate the missing data model into the observed data likelihood function. We investigate two approximate likelihood methods, bivariate pseudo-likelihood (BPL) of Sinha et al. (2011) and independent pseudo-likelihood (IPL) of Troxel et al. (1998), along with the exact likelihood, for analyzing longitudinal data with nonignorable and nonmonotone missing responses. Our numerical study shows that the BPL is more efficient than the IPL method in terms of both smaller biases and MSEs, especially when the within-subject correlation is high. Investigating the standard errors of estimators in an application with real world data, we also get similar findings. Overall, similarly to Sinha et al. (2011), our study confirms better performance of the BPL method over the IPL method when the number of longitudinal outcomes from a given subject is small.

Name thesis:degree_name

Master of Science (M.Sc.)

Level thesis:degree_level

Master's

Discipline thesis:degree_discipline

Statistics

Grantor dc:publisher

Carleton University

Year dc:date.issued

2021

Author dc:creator

• Izad Shenas, Seyed Abdolmotalleb

Statement dc:rights

• Copyright © 2021 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.

Language dc:language.iso

en

DOI

10.22215/etd/2020-14333

OAI identifier oai:identifier

oai:carleton.scholaris.ca:20.500.14718/41579