A Random‐Effects Approach to Generalized Linear Mixed Model Analysis of Incomplete Longitudinal Data

We propose a random-effects approach to missing values for generalized linear mixed model (GLMM) analysis of longitudinal data. The method converts a GLMM with missing covariates to another GLMM without missing covariates. The standard GLMM analysis tools then apply. The method applies, in particular, to the cases of linear mixed models (LMMs) and logistic regression. Performance of the method is evaluated empirically, and compared with alternative approaches, including the popular MICE procedure of multiple imputation (MI). Theoretical justification of the method is given, and explained, for the patterns observed in the simulation studies. Two real-data examples from healthcare studies are discussed.