Model selection in the weighted generalized estimating equations for longitudinal data with dropout.
We propose criteria for variable selection in the mean model and for the selection of a working correlation structure in longitudinal data with dropout missingness using weighted generalized estimating equations. The proposed criteria are based on a weighted quasi-likelihood function and a penalty term. Our simulation results show that the proposed criteria frequently select the correct model in candidate mean models. The proposed criteria also have good performance in selecting the working correlation structure for binary and normal outcomes. We illustrate our approaches using two empirical examples. In the first example, we use data from a randomized double-blind study to test the cancer-preventing effects of beta carotene. In the second example, we use longitudinal CD4 count data from a randomized double-blind study.
Advanced Medical Research Center, Aichi Medical University, 1-1, Yazakokarimata, Nagakute, Aichi 480-1195, Japan.|Department of Clinical Trial and Clinical Epidemiology, Faculty of Medicine, University of Tsukuba, 1-1-1, Tennodai, Tsukuba, Ibaraki 305-8575, Japan.
Masahiko
M
Gosho
D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D016013:Likelihood Functions; D008137:Longitudinal Studies; D015233:Models, Statistical; D010352:Patient Dropouts / Q000706:statistics & numerical data*
Correlation structure; Missingness; Quasi-likelihood; Robust variance