Imputation of missing covariates under a multivariate linear mixed model

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Informing sequential clinical decision-making through reinforcement learning: an empirical study (2010, methodology): “Since we expect the mean of the symptom measure PANSS to be smooth across time, we use a second method, based on a mixed effects model (Schafer 1997; Schafer and Yucel 2002), to re-impute individual monthly PANSS scores according to the desired smoothness constraint.”
Estimating the optimal dynamic antipsychotic treatment regime: Evidence from the sequential multiple assignment randomized CATIE Schizophrenia Study (2012, methodology): “To enforce smoothness over time in the mean symptom measurement, we use a Bayesian mixed effects model (Schafer, 1997; Schafer and Yucel, 2002) to impute missing time varying PANSS scores.”
Multiple Imputation of Multilevel Missing Data—Rigor Versus Simplicity (2015, methodology): “For this reason, different strategies to setup multilevel imputation models have been discussed in the literature (Liu, Taylor, & Belin, 1995; Schafer, 1997; Schafer & Yucel, 2002; Yucel, 2011).”
Missing data techniques for multilevel data: implications of model misspecification (2011, methodology): “We created 10 imputed data sets for each of the 1000 files, using the expectation maximization algorithm to provide starting values for parameter estimates [4] followed by imputation of values by DA [27,38].”