Missing data is an ever-present problem in clinical trials, which can destroy the balance provided by randomization and thereby bias treatment group comparisons. Data simulation has provided a powerful platform for comparing how well analytic methods perform with incomplete data. In contrast, methods of preventing missing data cannot be evaluated via simulation and actual clinical trials are not designed to assess factors that influence retention. Therefore, many confounding factors can mask or exaggerate differences in rates of missing data attributable to trial methods. Not surprisingly then, the literature contains more information on how to treat missing data than on how to prevent it.