Proving that a new treatment is more effective than current treatment can be difficult for rare conditions. Data from small randomised trials could, however, be made more robust by taking other related research into account

The need for randomised trials to establish that treatments are effective is well established. However, because the effects of new treatments are usually modest compared with standard treatment, large numbers of patients are needed to detect any genuine benefits. This means that, even for common cancers, studies often have to be multicentred to ensure enough patients are recruited in a reasonable time. The strategy for testing new treatments in rare cancers, where it is impossible to accrue large number of patients, is unclear. We extend Lilford and others' proposal that a bayesian statistical approach, using related information from earlier studies, would be useful in designing and subsequently summarising small randomised controlled trials.1 We suggest a scoring system for pooling this evidence and detail how this may be combined with hypothetical scenarios to assist in the design of, and justification for, a small randomised controlled trial.