The main purpose of this research was to evaluate use of Last Observation Carried Forward (LOCF) as an imputation method when persistent binary outcomes are missing in a Randomized Controlled Trial. A simulation study was performed to see the effect of dropout rate and type of dropout (random or associated with treatment arm) on Type I error and power. Properties of estimated event rates, treatment effect, and bias were also assessed. LOCF was also compared to two versions of complete case analysis - Complete1 (excluding all observations with missing data), and Complete2 (only carrying forward observations if the event is observed to occur). LOCF was not recommended because of the bias. Type I error was increased, and power was decreased. The other two analyses also had poor properties. LOCF analysis was applied to a mammogram dataset, with results similar to the simulation study.