Monitoring of large randomised clinical trials: a new approach with Bayesian methods

Background In judging whether or not to continue enrolling patients into a randomised clinical trial, most data-monitoring and ethics committees (DMEC s) rely on the p value for the difference in effect between the study group s. In the 1990s, two randomised controlled trials-one in patients with lung cancer and one in those with head and neck cancer-were instead monitored b y Bayesian methods. We assessed the value of this approach in the monitorin g of these clinical trials, Methods Before the trials opened, participating clinicians were asked their opinions on the expected difference between the study treatment (continuou s hyperfractionated accelerated radiotherapy [CHART]) and conventional radi otherapy. These opinions were used to form an "enthusiastic" and a "sceptic al" prior distribution. These prior distributions were combined with the tr ial data at each of the annual DMEC meetings. If, during monitoring, a resu lt in favour of CHART was seen, the DMEC was to decide whether the results were sufficiently convincing to persuade a sceptic that CHART was worthwhil e. Conversely, if there was apparently no or little difference, the DMEC wa s asked whether they thought the results sufficiently convincing to persuad e an enthusiast that CHART was not worthwhile. Findings At each of the annual meetings, the DMEC concluded that there was insufficient evidence to convert either sceptics or enthusiasts, and that t he trials should therefore remain open to recruitment. Neither trial was cl osed to recruitment earlier than planned. However if a conventional (p-valu e-based) stopping rule had been used, the lung-cancer trial would probably have been stopped. Interpretation This Bayesian approach to monitoring is simple to implement and straightforward for members of the DMEC to understand. In our opinion, it is more intuitively appealing than conventional approaches.