Statistics Notes: Missing outcomes in randomised trials

In most randomised trials, some patients fail to provide data for study endpoints.1 We have previously described the analysis of a trial of acupuncture versus sham acupuncture for the treatment of shoulder pain.2 All 52 randomised patients provided baseline data on pain and range of motion, but only 45 returned for follow-up testing. The statistical question is how to handle those seven patients with missing data. The most straightforward approach is simply to ignore the seven patients and do what is known as an “available case analysis” (often confusingly known as “complete case analysis”). As not all randomised patients are included in the analysis, this leads to reduced statistical power.1 A method that attempts to include all randomised patients is “last observation carried forward,” in which the last measurement obtained from the patient is used for all data points that were subsequently missed. This method is attractive because it is simple, but it has little else to recommend it. Substituting a missing data point with a value is known as “imputation,”1 and the data analyst needs a clear rationale for the …