Multiple imputation (MI) and analysis of imputed time-to-event data is implemented in a collection of SAS macros based on the methodology described in the following publications:
 Lipkovich I, Ratitch B, O’Kelly M (2016) Sensitivity to censored-at-random assumption in the analysis of time-to-event endpoints. Pharmaceutical Statistics 15(3):216-229
 Moscovici JL, Ratitch B (2017) Combining Survival Analysis Results after Multiple Imputation of Censored Event Times. PharmaSUG-2017 (available on-line https://www.pharmasug.org/proceedings/2017/SP/PharmaSUG-2017-SP05.pdf)
Briefly, the methods estimate multiple imputations via draws from the Bayesian posterior distribution of parameters of a model (piecewise exponential); or via bootstrapped versions of the input data with a standard inverse method translating estimated probability into time to event (Cox and Kaplan-Meier). Hazards can be subjected to increase/decrease via user-specified amount delta; reference-based imputed survival can be implemented by estimating the imputation model based on a user-specified subset of observed subjects (piecewise exponential and Kaplan-Meier); or by user specification of the treatment group parameter to be used when calculating the imputed time to event (Cox).
The macros can be downloaded here: Package_Release_V3 final.