Generalized estimating equation (GEE) on binary longitudinal data

Devita Putri Mardyanti, Rohmatul Fajriyah

Abstract


Abstract. Binary logistic regression is a regression in which the response variable is binary with one or more predictor variables being both categorical and continuous. It can be applied to longitudinal data, where the observations are cross-sectional units over several periods of time. These repeated observations cause autocorrelation and needs to be addressed by implementing the Generalized Estimating Equation (GEE) method. The study aims to apply GEE and choose the best correlation structure based on the QIC value, where the data is the sputum status of pulmonary tuberculosis patients at PKU Muhammadiyah Hospital at Bantul, Yogyakarta.  Based on the analysis then the model of sputum status is , with the correlation structure is autoregressive (1).


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References


Agresti A 2002 An Introduction to Categorical Data Analysis (New York: John Wiley and Sons)

Hedeker D and Gibbons R D 2006 Longitudinal Data Analysis (New York: John Wiley and Sons)

Kleinbaum D G and Klein M 2010 Logistic Regression A Self-Learning Text 3rd editions (New York: Springer)

Octaviana F A 2017 Pemodelan Status Bekerja Ibu Rumah Tangga Menggunakan Model Multilevel dengan Respon Biner Tesis (Surabaya: Institut Teknologi Sepuluh Nopember)

Swan T 2006 Generalized Estimating Equation when The Respons Variable Has a Tweedle Distribution: In Application for Multi-site Rainfall Modelling (Toowoomba : Department of Mathematics and Computing of The University of Southern Queensland). Retreived https://core.ac.uk/download/pdf/11036965.pdf access date Aug, 13th 2017

Twisk J 2003 Applied Longitudinal Data Analysis for Epidemiology (New York: Cambridge University Press)


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