Increased survival is a common goal of cancer clinical trials. Owing to the long periods of observation and follow-up to assess patient survival outcome, it is difficult to use outcome-adaptive randomization in these trials. In practice, often information about a short-term response is quickly available during or shortly after treatment, and this short-term response is a good predictor for long-term survival. For example, complete remission of leukemia can be achieved and measured after a few cycles of treatment. It is a short-term response that is desirable for prolonging survival. We propose a new design for survival trials when such short-term response information is available. We use the short-term information to 'speed up' the adaptation of the randomization procedure. We establish a connection between the short-term response and the long-term survival through a Bayesian model, first by using prior clinical information, and then by dynamically updating the model according to information accumulated in the ongoing trial. Interim monitoring and final decision making are based upon inference on the primary outcome of survival. The new design uses fewer patients, and can more effectively assign patients to the better treatment arms. We demonstrate these properties through simulation studies.
(c) 2009 John Wiley & Sons, Ltd.