Some missing data are anticipated. If data are missing at random, there will be a loss in efficiency of the proposed analyses, but bias will not be introduced into the study by not accounting for the missing data. We have assumed that we will have outcome information on 85% follow-up on LTRC participants. Per Appendix C this is at least 1300 participants through 2010 and 2700 through 2014. A review of power curves and tables for sample numbers up to 1600 participants, suggests that, there will be sufficient power to address the study objectives, even when the sample of evaluable participants is lower than 1000.
However, if data are not missing at random, there could be a bias in some of the estimation and inference routines used in this study. We will use the multiple imputation procedure developed by Rubin to correct for this type of bias (15).