| Affiliation: | 1. School of Statistics, East China Normal University, Shanghai, China;2. Key Laboratory of Advanced Theory and Application in Statistics and Data Science – MOE, East China Normal University, Shanghai, China School of Statistics, East China Normal University, Shanghai, China;3. Key Laboratory of Advanced Theory and Application in Statistics and Data Science – MOE, East China Normal University, Shanghai, China |
| Abstract: | A biosimilar drug is a biological product that is highly similar to and at the same time has no clinically meaningful difference from licensed product in terms of safety, purity, and potency. Biosimilar study design is essential to demonstrate the equivalence between biosimilar drug and reference product. However, existing designs and assessment methods are primarily based on binary and continuous endpoints. We propose a Bayesian adaptive design for biosimilarity trials with time-to-event endpoint. The features of the proposed design are twofold. First, we employ the calibrated power prior to precisely borrow relevant information from historical data for the reference drug. Second, we propose a two-stage procedure using the Bayesian biosimilarity index (BBI) to allow early stop and improve the efficiency. Extensive simulations are conducted to demonstrate the operating characteristics of the proposed method in contrast with some naive method. Sensitivity analysis and extension with respect to the assumptions are presented. |
