I have a question regarding model building for a large dataset including about 5000 Subjects. I want to fit a LMEM including multiple variables and I have repeated measurements in time. But for some of the subjects (around 1200, means <25%) I only have one measurement. This was no problem when fitting a simple LMEM just including a random intercept as the dataset is large enough. However, I ended up in identifiability problems and non-convergence when adding a random slope to the model. So im wondering what's more common: Removing the subjects only providing one measurment and estimating a model with random intercept and slope or keeping the total data set as it is and just using a random intercept.
Actually the results concerning the fixed effects are quite similar but I want to go the correct and more-standard way. I am really wondering how to decide whether to use only random intercept or random intercept and slope.
Thanks a lot!