Simple generalized estimating equations (GEEs) and weighted generalized estimating equations (WGEEs) in longitudinal studies with dropouts: guidelines and implementation in R

• Published in: Statistics in medicine Vol. 35; no. 19; pp. 3424 - 3448
• Main Authors: Salazar, Alejandro, Ojeda, Begoña, Dueñas, María, Fernández, Fernando, Failde, Inmaculada
• Format: Journal Article
• Language: English
• Published: England Blackwell Publishing Ltd 30.08.2016; Wiley Subscription Services, Inc
• Subjects: Cardiovascular disease; Clinical trials; Data Interpretation, Statistical; dropout; Epidemiology; generalized estimating equations; Generalized linear models; Heart Diseases; Humans; Longitudinal Studies; missing data; Models, Statistical; Patient Dropouts; Quality of Life; Statistical methods; weighted generalized estimating equations; weighted generalized estimating equations; generalized estimating equations; missing data; dropout; longitudinal studies
• ISSN: 0277-6715; 1097-0258
• DOI: 10.1002/sim.6947

Missing data are a common problem in clinical and epidemiological research, especially in longitudinal studies. Despite many methodological advances in recent decades, many papers on clinical trials and epidemiological studies do not report using principled statistical methods to accommodate missing data or use ineffective or inappropriate techniques. Two refined techniques are presented here: generalized estimating equations (GEEs) and weighted generalized estimating equations (WGEEs). These techniques are an extension of generalized linear models to longitudinal or clustered data, where observations are no longer independent. They can appropriately handle missing data when the missingness is completely at random (GEE and WGEE) or at random (WGEE) and do not require the outcome to be normally distributed. Our aim is to describe and illustrate with a real example, in a simple and accessible way to researchers, these techniques for handling missing data in the context of longitudinal studies subject to dropout and show how to implement them in R. We apply them to assess the evolution of health‐related quality of life in coronary patients in a data set subject to dropout. Copyright © 2016 John Wiley & Sons, Ltd.