Model Selection for Generalized Estimating Equations Accommodating Dropout Missingness

• Published in: Biometrics Vol. 68; no. 4; pp. 1046 - 1054
• Main Authors: Shen, Chung‐Wei, Chen, Yi‐Hau
• Format: Journal Article
• Language: English
• Published: Malden, USA Blackwell Publishing Inc 01.12.2012; Wiley-Blackwell; Blackwell Publishing Ltd
• Subjects: Algorithms; Analytical estimating; BIOMETRIC METHODOLOGY; Biometrics; biometry; Biometry - methods; Clinical outcomes; Correlations; Data Interpretation, Statistical; Drug design; Epidemiologic Methods; equations; Estimating techniques; Longitudinal data; Longitudinal Studies; Missing data; Modeling; Models, Statistical; Parametric models; Regression Analysis; Repeated measures; Sample Size; School dropouts
• ISSN: 0006-341X; 1541-0420; 1541-0420
• DOI: 10.1111/j.1541-0420.2012.01758.x

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.