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Joint model selection of marginal mean regression and correlation structure for longitudinal data with missing outcome and covariates
Author(s) -
Shen ChungWei,
Chen YiHau
Publication year - 2018
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.201600195
Subject(s) - covariate , statistics , covariance , missing data , mathematics , marginal model , regression analysis , correlation , model selection , regression , econometrics , selection (genetic algorithm) , outcome (game theory) , analysis of covariance , random effects model , computer science , artificial intelligence , meta analysis , geometry , mathematical economics , medicine
This work develops a joint model selection criterion for simultaneously selecting the marginal mean regression and the correlation/covariance structure in longitudinal data analysis where both the outcome and the covariate variables may be subject to general intermittent patterns of missingness under the missing at random mechanism. The new proposal, termed “joint longitudinal information criterion” (JLIC), is based on the expected quadratic error for assessing model adequacy, and the second‐order weighted generalized estimating equation (WGEE) estimation for mean and covariance models. Simulation results reveal that JLIC outperforms existing methods performing model selection for the mean regression and the correlation structure in a two stage and hence separate manner. We apply the proposal to a longitudinal study to identify factors associated with life satisfaction in the elderly of Taiwan.