The R package dejaVu, now available on CRAN, implements controlled based multiple imputation for count data, as proposed by Keene, Oliver N., et al. “Missing data sensitivity analysis for recurrent event data using controlled imputation.” Pharmaceutical Statistics 13:4 (2014): 258-264.
When used to analyse an existing partially observed dataset, the package first fits a negative binomial regression model to the observed data, assuming MAR. Multiple imputations of the counts in the periods after subjects dropout are then generated, under a user chosen assumption. Options include MAR, and the jump to reference and copy reference MNAR assumptions. Users can also write and use their own imputation mechanisms with the package.
The package was developed by the Scientific Computing and Statistical Innovation groups of the Advanced Analytics Centre at AstraZeneca.
A SAS implementation of the same methods, developed by James Roger, is available here.