Missing Data Mixed Effects Modelling for Repeated Measures I am currently working on a problem where I try to explain within subject variance in an outcome using multiple other variables. In R setting up the model looks like this
lmFull<-lmer(outcome ~ (1|subject) + pred1 + pred2 +pred3 ... pred40)

The problem is that there is some mild missingness in each predictor variables. Thus, using listwise deletion (the default setting of lmer) many cases are lost, since due to the number of predictors chances are high that at least one is missing.
I have googled this problem but can only find examples claiming that this is not an issue in mixed modelling since it uses the long format. This, however, is only true if there are few predictors.
One obvious solution would be to use FIML (Full Information Maximum Likelihood). However, some of my predictors are ordinal and thus not normally distributed. Setting up a valid joint distribution for all my predictors will be very cumbersome.
One quick and dirty solution might be to impute the values with the within subject means.
Any thoughts? Is there a recommended approach?
 A: In his book Stef van Buuren describes the difficulties in multilevel modelling when a level-1 or level-2 predictor is missing. He advices to use 2l.pmm from miceadds, and setting the value to 3 in the predictor matrix as outlined here under '7.10.2 Random intercepts, missing level-1 predictor'. This adds the cluster means for the imputed variable to the model. According to this, a value of 3 in the predictor matrix of mice means 'imputation model with a fixed effect, random intercept and cluster means'.
This seems to be an update on this tutorial where the accepted answer is described. As Stef van Buuren also mentions, alternatives would be ad-hoc solutions, eg. listwise deletion (in your case obviously does not make sense), or multilevel imputation with joint models, eg with the R package jomo.
P.S.: To add to OP's question of long vs wide: Stef van Buuren also mentions that mixed models can handle missing data in the outcome easily (see here).

The multilevel model is actually “made to solve” the problem of missing values in the outcome. (...) Missing outcome data are easily handled in modern likelihood-based methods (...). Mixed-effects models can be fit with maximum-likelihood methods, which take care of missing data in the dependent variable. This principle can be extended to address missing data in explanatory variables in (multilevel) software for structural equation modeling like Mplus (Muthén, Muthén, and Asparouhov 2016) and gllamm (Rabe-Hesketh, Skrondal, and Pickles 2002).

