Generalized linear mixed models with informative dropouts and missing covariates

作     者：Wu, Kunling Wu, Lang 

作者机构：Univ British Columbia Dept Stat Vancouver BC V6T 1Z2 Canada Harvard Univ Sch Publ Hlth Ctr Biostat AIDS Res Boston MA 02115 USA 

出 版 物：《METRIKA》 (米制)

年 卷 期：2007年第66卷第1期

页      面：1-18页

核心收录：

学科分类：0202[经济学-应用经济学] 02[经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 

主　　题：PX-EM algorithm Gibbs sampling linearization rejection sampling 

摘      要：Generalized linear mixed models (GLMM) are useful in many longitudinal data analyses. In the presence of informative dropouts and missing covariates, however, standard complete-data methods may not be applicable. In this article, we consider a likelihood method and an approximate method for GLMM with informative dropouts and missing covariates. The methods are implemented by Monte-Carlo EM algorithms combined with Gibbs sampler. The approximate method may lead to inconsistent estimators but is computationally more efficient than the likelihood method. The two methods are evaluated via a simulation study for longitudinal binary data, and appear to perform reasonably well. A dataset on mental distress is analyzed in details.