Generalized Estimating Equation

M. Ataharul Islam () and Rafiqul I. Chowdhury
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M. Ataharul Islam: University of Dhaka, Institute of Statistical Research and Training (ISRT)
Rafiqul I. Chowdhury: University of Dhaka, Institute of Statistical Research and Training (ISRT)

Chapter Chapter 12 in Analysis of Repeated Measures Data, 2017, pp 161-167 from Springer

Abstract: Abstract The generalized estimating equation (GEE) uses a quasi-likelihood approach for analyzing data with correlated outcomes. This is an extension of GLM and uses quasi-likelihood method for cluster or repeated outcomes. If observations on outcome variable are repeated, it is likely that the observations are correlated. In addition, non-normality of outcome variables is a common phenomenon in real-life problems. In such situations, use of quasi-likelihood estimating equations provides necessary methodological support for estimating parameters of a regression model. The GEE is a marginal model approach for analyzing repeated measures data developed by Zeger and Liang (1986) and Liang and Zeger (1986). This approach can be considered as a semiparametric approach because it does not require full specification of the underlying joint probability distribution for repeated outcome variables rather assumes likelihood for marginal distribution and a working correlation matrix. The correlation matrix represents the correlation between observations in clusters observed from panel, longitudinal, or family studies. In this chapter, an overview of GEE is presented.

Date: 2017
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DOI: 10.1007/978-981-10-3794-8_12

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