I am working on a longitudinal data set, with each person being measured 8 times on each dependent variable. Some of the dependent variables are continuous; some are counts (mostly with means between 50 and 100) and one is dichotomous. There are 3 independent variables: The main one is continuous and ther are two binary covariates. I intend to use multilevel models. I am not (at least for now) interested in relationships among the dependent variables.
About 75% of the subjects have complete data. The missing data patterns vary widely and are not monotone, but most of the missing data is at the later time points.
From substantive considerations, it seems very likely that the data are MAR, and the means on the variables are mostly pretty similar (with some outliers due to small sample sizes in some patterns).
My usual approach here would be to use MLMs without doing imputation, but a little research has shown me that some researchers recommend multiple imputation when there is missing data even in MLMs with MAR missingness.
I prefer to use SAS but can also use R, but software is not my main concern.
I'd be interested in any recommendations or pointers to review articles.
EDIT: There is no missing data on the independent variables, only the outcomes