What is missing from my missing data plan?

U nder the intention-to-treat principle, all randomized subjects should be analyzed according to their randomly assigned treatment, regardless of treatment actually received or protocol compliance. Adherence to this principle requires that even subjects with missing outcome data be included in the analysis; in fact, the exclusion of such subjects can have important implications on power and bias. Statistical methods for dealing with missing data exist, but many questions remain unclear. Much statistical research has been devoted to the development and assessment of various methods for handling missing data. 1 The choice of appropriate methodology requires assumptions on the mechanism underlying the missing data. All of these decisions should be made a priori, preferably before the trial starts but certainly before unblind-ing the trial. Related conversations between clinical investigators and the study statistician during the design phase often focus on more practical questions. Is there some threshold for the missing data rate below which the trial's conclusions are unlikely to be affected? Under what circumstances can the missing data be excluded from the analysis without biasing estimation, or is imputation always the preferred approach? In this article, we discuss implications of missing outcome data from a practical standpoint. We describe potential reasons for missing data and suggest strategies to minimize its occurrence. We also present common imputation approaches and emphasize that because none of these approaches are universally preferred, the best analytic plan includes a series of sensitivity analyses. In any longitudinal trial where subjects are followed over some extensive period of time, lengthy follow-up makes missing data somewhat unavoidable. In stroke clinical trials, the primary outcome assessment often occurs at 90 days although there is evidence to suggest that additional follow-up may be beneficial. Subjects may expire, or withdraw informed consent, before primary outcome ascertainment. Subjects may become lost to the study team because of incomplete contact information or because they move out of the relevant catchment area. When developing an approach for handling missing data, the best defense is a good offense; that is, the best approach is to proactively prevent the occurrence of missing data. Various protocol strategies can be considered, based on careful consideration as to why missing data might occur in a population. The first such strategy is to recognize the distinction between discontinuation from study treatment and dis-continuation from the study; subjects may discontinue study treatment for a variety of reasons, but such subjects remain …