I currently have a dataset which contains variables with different degrees of missingingness. One of the key variables for my analysis has about 12% of the values Missing Not at Random (MNAR).
From previous research I have done, I gather that most current MI methods assume a MAR mechanism, but could still be useful in MNAR scenarios. The worst possible solution to dealing with MNAR data is still a complete case analysis.
I've heard that you can deal with MNAR by using Pattern Mixture Models and Selection Models, but I do not have any experience with using these in R (which is the software I usually use for analysis). Alternatively, I have seen that the
mice package has a method called
mice.impute.ri which can be used with 'non-ignorable data'. I also saw that there is an older package,
SensMice (from 2011), which performs sensitivity analysis after the
mice imputation has already been done. However, it no longer seems to be compatible with my version of Rstudio and there doesn't seem to be much recent word on this package.
Does anyone have practical advice on how I can conduct my imputation for this variable? It is preferable for advice that relates to specific R packages that might be useful. Otherwise, theoretical advice or steps to take in the imputation process are also appreciated.