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Different methods for handling incomplete longitudinal binary outcome due to missing at random dropout
Institution:1. School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Private Bag X01 Scottsville 3209, Pietermaritzburg, South Africa;2. Hasselt University, I-BioStat, 3500 Hasselt, Belgium;3. KU Leuven - University of Leuven, 3000 Leuven, Belgium;1. Faculty of Engineering, Oita University, 700 Dannoharu, Oita 870-1192, Japan;2. Faculty of Environmental Engineering, The University of Kitakyushu, 1-1 Hibikino, Wakamatsu, Kitakyushu, Fukuoka 870-1192, Japan;3. Graduate School of Oita University, 700 Dannoharu, Oita 870-1192, Japan;1. Institute of Public Health, Charité-Universitätsmedizin Berlin, Berlin, Germany;2. Institute of Medical Psychology, Charité-Universitätsmedizin Berlin, Berlin, Germany;3. Department of Psychiatry and Psychotherapy, Charité-Universitätsmedizin Berlin, Berlin, Germany;4. Social and Preventive Medicine, University of Potsdam, Potsdam, Germany;5. Department of Psychiatry, University of Münster, Germany;6. Department of Internal Medicine and Cardiology, Charité-Universitätsmedizin Berlin, Berlin, Germany;7. Department of Cardiovascular Medicine, University Hospital Münster, Germany;1. Department of Neurology, Jagiellonian University Medical College, Kraków, Poland;2. Department of Diagnostics, John Paul II Hospital, Kraków, Poland;3. Faculty of Medicine and Life Sciences, Hasselt University, Hasselt, Belgium;4. Heart Centre Hasselt, Jessa Hospital, Hasselt, Belgium;5. Department of Cardiology, Antwerp University Hospital, Edegem, Belgium;6. Institute of Public Health, Faculty of Health Sciences, Jagiellonian University Medical College, Kraków, Poland;7. Institute of Cardiology, Jagiellonian University Medical College, Kraków, Poland;1. Korteweg–de Vries Institute for Mathematics, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands;2. The University of Queensland, St Lucia, Queensland, Australia;4. Amsterdam Business School, Faculty of Economics and Business, University of Amsterdam, Amsterdam, The Netherlands
Abstract:This paper compares the performance of weighted generalized estimating equations (WGEEs), multiple imputation based on generalized estimating equations (MI-GEEs) and generalized linear mixed models (GLMMs) for analyzing incomplete longitudinal binary data when the underlying study is subject to dropout. The paper aims to explore the performance of the above methods in terms of handling dropouts that are missing at random (MAR). The methods are compared on simulated data. The longitudinal binary data are generated from a logistic regression model, under different sample sizes. The incomplete data are created for three different dropout rates. The methods are evaluated in terms of bias, precision and mean square error in case where data are subject to MAR dropout. In conclusion, across the simulations performed, the MI-GEE method performed better in both small and large sample sizes. Evidently, this should not be seen as formal and definitive proof, but adds to the body of knowledge about the methods’ relative performance. In addition, the methods are compared using data from a randomized clinical trial.
Keywords:Multiple imputation GEE  Weighted GEE  Generalized linear mixed model (GLMM)  Incomplete longitudinal binary outcome  Missing at random (MAR)
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