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

We propose a random-effects approach to missing values for linear mixed model (LMM) analysis. The method converts a LMM with missing covariates to another LMM without missing covariates. The standard LMM analysis tools for longitudinal data then apply. Performance of the method is evaluated empirically, and compared with alternative approaches, including the popular MICE procedure of multiple imputation. Theoretical explanations are given for the patterns observed in the simulation studies. A real-data example is discussed.