Monitoring the rates of composite events with censored data in phase II clinical trials.

Abstract

In many phase II clinical trials, interim monitoring is based on the probability of a binary event, response, defined in terms of one or more time-to-event variables within a time period of fixed length. Such outcome-adaptive methods may require repeated interim suspension of accrual in order to follow each patient for the time period required to evaluate response. This may increase trial duration, and eligible patients arriving during such delays either must wait for accrual to reopen or be treated outside the trial. Alternatively, monitoring may be done continuously by ignoring censored data each time the stopping rule is applied, which wastes information. We propose an adaptive Bayesian method that eliminates these problems. At each patient's accrual time, an approximate posterior for the response probability based on all of the event-time data is used to compute an early stopping criterion. Application to a leukemia trial with a composite event shows that the method can reduce trial duration substantially while maintaining the reliability of interim decisions.