A Bayesian Stopping Rule for Sequential Monitoring of Serious Adverse Events

In an ongoing clinical trial, there will always be a risk for unanticipated critical safety problems, such as excessive occurrence of serious adverse events. When such a problem arises, the trial administrators must conduct an immediate evaluation to determine whether the trial should be terminated to protect patients. This decision is complicated but may be aided by statistical stopping rules. Sequential stopping rules are appropriate for immediate decisions, but frequentist approaches may not be useful because the unknown truncated end of the trial makes it impossible to define type I errors. Thus, a Bayesian stopping rule is proposed that is based on the posterior distribution with an informative prior distribution, and a guideline to construct this stopping rule is presented. Some operating characteristics are evaluated and compared with those of the modified sequential probability ratio test (SPRT), the maximized SPRT, and Pocock’s test. The proposed method has flexibility for construction and could provide a more desirable performance than the other compared methods.