I am working with a dataset of repeated (x4) observations on 100 subjects.
The outcome is zero-inflated and the data appears to be modelled well by a mixed effects zero-inflated negative binomial model with random intercepts for subjects.
However, approximately 20% of the outcome variable is missing, so I am investigating imputation methods that can deal with this. There are 2 covariates in the analysis model and a further 6 auxiliary variables that can be used for imputation - all of which have very low levels of missingness.
I have tried the
mice package in R which supports random effects, but does not support the negative binomial distribution.
The only method I have found useful so far, is random hot-deck imputation, which I have tried in R (with the package
StatMatch) and Stata (with the package
hotdeck) both of which seem to produce reasonable results
Is there any other package/system that can be used to impute values for this kind of model ? I have experience with R and Stata, and SAS is also available to me (though I don't have any experience with it)