A Bayesian group sequential design for randomized biosimilar clinical trials with adaptive information borrowing from historical data.

At the time of developing a biosimilar, the reference product has been on market for years and thus ample data are available on its efficacy and characteristics. We develop a Bayesian adaptive design for randomized biosimilar clinical trials to leverage the rich historical data on the reference product. This design takes a group sequential approach. At each interim, we employ the elastic meta-analytic-predictive (EMAP) prior methodology to adaptively borrow information from the historical data of the reference product to make go/no-go decision based on Bayesian posterior probabilities. In addition, the randomization ratio between the test and reference arms is adaptively adjusted at the interim with the goal to balance the sample size of the two arms at the end of trials. Simulation study shows that the proposed Bayesian adaptive design can substantially reduce the sample size of the reference arm, while achieving comparable power as the traditional randomized clinical trials that ignore the historical data. We apply our design to a biosimilar trial for treating breast cancer patients.