title: Model selection for marginal regression analysis of longitudinal data with missing observations and covariate measurement error.
abstract: Missing observations and covariate measurement error commonly arise in longitudinal data. However, existing methods for model selection in marginal regression analysis of longitudinal data fail to address the potential bias resulting from these issues. To tackle this problem, we propose a new model selection criterion, the Generalized Longitudinal Information Criterion, which is based on an approximately unbiased estimator for the expected quadratic error of a considered marginal model accounting for both data missingness and covariate measurement error. The simulation results reveal that the proposed method performs quite well in the presence of missing data and covariate measurement error. On the contrary, the naive procedures without taking care of such complexity in data may perform quite poorly. The proposed method is applied to data from the Taiwan Longitudinal Study on Aging to assess the relationship of depression with health and social status in the elderly, accommodating measurement error in the covariate as well as missing observations.
authors.affiliation: Department of Mathematics, National Chung Cheng University, Chia-Yi 621, Taiwan, R.O.C. cwshen@ccu.edu.tw.
authors.forename: Chung-Wei
authors.initials: CW
authors.lastname: Shen
authors.affiliation: Institute of Statistical Science, Academia Sinica, Taipei 11529, Taiwan, R.O.C.
authors.forename: Yi-Hau
authors.initials: YH
authors.lastname: Chen
mesh_terms: D000375:Aging; D003627:Data Interpretation, Statistical; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D012044:Regression Analysis; D012107:Research Design
keywords: Errors-in-variables; Generalized estimating equations; Generalized method of moments; Information criterion; Missing atrandom