if ~2% of my data is missing on the outcome (continuous scale), out of a total of 200, two in control and three in intervention group, do I need to impute? Or can I make a case that with such small missing data per-protocol (PP) approximates Intention-to-treat (ITT). If I do impute, am I better off doing multiple imputation (MI) or can I do last observation carried forward (LOCF)
The purpose of the analysis is to basically compare the improvement in a score from baseline to time point 5 and there are 3 other time points between baseline and time point 5, where scores were taken. So, I can carry the observation at 4th time point forward instead of MI for the 5 missing data points (LOCF).
So which of the following is preferable: 1. Multiple Imputation 2. LOCF 3. Make a case PP was as good as ITT because the missing data was minimal and there was nothing extra ordinary about the 3 patients that had missing data at time point 5.