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

In judging whether or not to continue enrolling patients into a randomised clinical trial, most datamonitoring and ethics committees (DMECs) rely on the p value for the difference in effect between the study groups. 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 by Bayesian methods. We assessed the value of this approach in the monitoring of these clinical trials. Before the trials opened, participating clinicians were asked their opinions on the expected difference between the study treatment (continuous hyperfractionated accelerated radiotherapy [CHART]) and conventional radiotherapy. These opinions were used to form an “enthusiastic” and a “sceptical” prior distribution. These prior distributions were combined with the trial data at each of the annual DMEC meetings. If, during monitoring, a result in favour of CHART was seen, the DMEC was to decide whether the results were sufficiently convincing to persuade a sceptic that CHART was worthwhile. Conversely, if there was apparently no or little difference, the DMEC was asked whether they thought the results sufficiently convincing to persuade an enthusiast that CHART was not worthwhile. At each of the annual meetings, the DMEC concluded that there was insufficient evidence to convert either sceptics or enthusiasts, and that the trials should therefore remain open to recruitment. Neither trial was closed to recruitment earlier than planned. However if a conventional (p-value-based) stopping rule had been used, the lung-cancer trial would probably have been stopped. 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.