Model Selection for Generalized Estimating Equations Accommodating Dropout Missingness

Summary The generalized estimating equation (GEE) has been a popular tool for marginal regression analysis with longitudinal data, and its extension, the weighted GEE approach, can further accommodate data that are missing at random (MAR). Model selection methodologies for GEE, however, have not been systematically developed to allow for missing data. We propose the missing longitudinal information criterion (MLIC) for selection of the mean model, and the MLIC for correlation (MLICC) for selection of the correlation structure in GEE when the outcome data are subject to dropout/monotone missingness and are MAR. Our simulation results reveal that the MLIC and MLICC are effective for variable selection in the mean model and selecting the correlation structure, respectively. We also demonstrate the remarkable drawbacks of naively treating incomplete data as if they were complete and applying the existing GEE model selection method. The utility of proposed method is further illustrated by two real applications involving missing longitudinal outcome data.     

Generalized Estimating Equations in Controlled Clinical Trials: Hypotheses Testing

Generalized estimating equations (GEE) for the analysis of clustered data have gained increasing popularity. Recently, the first monograph on this method has been published. GEE have been repeatedly applied in controlled clinical trials. They have, however, been generally used as secondary or supplementary analysis. Instead, the primary analysis was mostly based on a classical method that usually ignored the clustered – mostly longitudinal – nature of the data. In this paper, we discuss the applicability of GEE as primary analysis in controlled clinical trials. From theoretical results in the literature, we derive recommendations how GEE should be used in therapeutic studies for testing statistical hypotheses. We hope that our paper is the starting point for a thorough discussion on the most appropriate analysis of controlled clinical trials with clustered dependent variables. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)