Model Selection for Generalized Estimating Equations Accommodating Dropout Missingness
Version of Record online: 29 MAR 2012
© 2012, The International Biometric Society
Volume 68, Issue 4, pages 1046–1054, December 2012
How to Cite
Shen, C.-W. and Chen, Y.-H. (2012), Model Selection for Generalized Estimating Equations Accommodating Dropout Missingness. Biometrics, 68: 1046–1054. doi: 10.1111/j.1541-0420.2012.01758.x
- Issue online: 21 DEC 2012
- Version of Record online: 29 MAR 2012
- Received August 2011. Revised December 2011. Accepted January 2012.
- Longitudinal data;
- Missing data;
- Repeated measures
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.