Bayesian optimal phaseIIclinical trial design with time-to-event endpoint

We propose a Bayesian optimal phase II (BOP2) design for clinical trials with a time-to-event endpoint (eg, progression-free survival [PFS]) or co-primary endpoints consisted of a time-to-event endpoint and a categorical endpoint (eg, PFS and toxicity). We use an exponential-inverse gamma model to model the time to event. At each interim, the go/no-go decision is made by comparing the posterior probabilities of the event of interest with an adaptive probability cutoff. The BOP2 design is flexible in the number of interim looks and applicable to both single-arm and two-arm trials. The design maximizes the power for detecting effective treatments, with a well-controlled type I error, thereby bridging the gap between Bayesian designs and frequentist designs. The BOP2 design is easy to implement. Its stopping boundary can be enumerated and included in study protocol before the onset of the trial for single-arm studies. Simulation studies show that the BOP2 design has favorable operating characteristics, with higher power and lower risk of incorrectly terminating the trial than some Bayesian phase II designs. The software to implement the BOP2 design will be freely available at .