Non-Responder Imputation (NRI) is a common statistical approach for the analysis of binary efficacy variables in immunology clinical trials. These variables can take values of 'Achieved' or 'Not Achieved'. According to the NRI rule, all missing values, for any reason including discontinuation from study or switching to rescue medications, are considered as 'Not Achieved'. Even though NRI approach is simple to implement and perceived as conservative, it may cause biased estimation of treatment effect in certain situations. Multiple imputation (MI) is becoming a popular methodology as sensitivity analysis in clinical trials when dealing with missing data. In this research, impact on statistical power between the MI and NRI approaches has been evaluated, along with the comparison to observed case (OC) approach, in which missing data will not be imputed and only the observed data will be used. Different scenarios have been explored and simulation results show that the MI method appears to be an appropriate choice as a sensitivity analysis to NRI when dealing with ignorable missing data.