A calibrated power prior approach to borrow information from historical data with application to biosimilar clinical trials

A biosimilar product refers to a follow-on biologic that is intended to be approved for marketing on the basis of biosimilarity to an existing patented biological product (i.e. the reference product). To develop a biosimilar product, it is essential to demonstrate biosimilarity between the follow-on biologic and the reference product, typically through two-arm randomization trials. We propose a Bayesian adaptive design for trials to evaluate biosimilar products. To take advantage of the abundant historical data on the efficacy of the reference product that is typically available at the time that a biosimilar product is developed, we propose the calibrated power prior, which allows our design to borrow information adaptively from the historical data according to the congruence between the historical data and the new data collected from the current trial. We propose a new measure, the Bayesian biosimilarity index, to measure the similarity between the biosimilar product and the reference product. During the trial, we evaluate the Bayesian biosimilarity index in a group sequential fashion on the basis of the accumulating interim data and stop the trial early once there is enough information to conclude or reject the similarity. Extensive simulation studies show that the design proposed has higher power than traditional designs. We applied the design to a biosimilar trial for treating rheumatoid arthritis.