Debusho, Legesse Kassa2015-01-192015-01-192014/12/122014Sepato, SM 2014, Generalized linear mixed model and generalized estimating equation for binary longitudinal data, MSc Dissertation, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/43143>M14/9/177http://hdl.handle.net/2263/43143Dissertation (MSc)--University of Pretoria, 2014.The most common analysis used for binary data is generalised linear model (GLM) with either a binomial or bernoulli distribution using either a logit, probit, complementary log-log or other type of link functions. However, such analyses violate the independence assumption if the binary data are measured repeatedly over time at the same subject or site. Failure to take into account the correlation can lead to incorrect estimation of regression parameters and the estimates are less efficient, particularly when the correlations are large. Therefore, to obtain the most efficient estimates that are also unbiased the methods that incorporate correlations (McCullagh and Nelder, 1989) should be used. Two of the statistical methodologies that can be used to account for this correlation for the longitudinal data are the generalized linear mixed models (GLMMs) and generalized estimating equation (GEE). The GLMM method is based on extending the fixed effects GLM to include random effects and covariance patterns. Unlike the GLM and GLMM methods, the GEE method is based on the quasi-likelihood theory and no assumption is made about the distribution of response observations (Liang and Zeger, 1986). The main objective of the study is to investigate the statistical properties and limitations of these three approaches, i.e. GLM, GLMMs and GEE for analyzing longitudinal data through use of a binary data from an entomology study. The results reaffirms the point made by these authors that misspecification of working correlation in GEE approach would still give consistent regression parameter estimates. Further, the results of this study suggest that even with small correlation, ignoring a random effects in a binary model can lead to inconsistent estimation.en© 2014 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria.UCTDDissertation21272752