The Application of Last Observation Carried Forward in the Persistent Binary Case

The main purpose of this research is 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 evaluate the effect of equal event rates and equal/unequal dropout rates on Type I error. 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). The results showed that 1) If the dropout rates were equal, the three analysis methods all had appropriate Type I error; 2) If the dropout rates were unequal, the Type I error was much greater than 0.05 in both LOCF and Complete2 analysis; 3) Regardless of dropout rates, the estimated mean event rate was underestimated in the LOCF analysis and overestimated in the Complete2 analysis, while Complete1 analysis had the closest estimated mean event rate to the true rate; 4) Compared to the study with no event at the first time point, the estimated mean event rate was underestimated less in the LOCF analysis and overestimated more in the Complete2 analysis when an event could occur at the first time point. LOCF analysis was applied to a mammogram dataset, where the LOCF method underestimated the final event rate.