Comparative Effectiveness Research using Bayesian Adaptive Designs for Rare Diseases: Response Adaptive Randomization Reusing Participants

Abstract

Slow accrual rate is a major challenge in clinical trials for rare diseases and is identified as the most frequent reason for clinical trials to fail. This challenge is amplified in comparative effectiveness research where multiple treatments are compared to identify the best treatment. Novel efficient clinical trial designs are in urgent need in these areas. Our proposed response adaptive randomization (RAR) reusing participants trial design mimics the real-world clinical practice that allows patients to switch treatments when desired outcome is not achieved. The proposed design increases efficiency by two strategies: (i) Allowing participants to switch treatments so that each participant can have more than one observation and hence it is possible to control for participant specific variability to increase statistical power; and (ii) Using RAR to allocate more participants to the promising arms such that ethical and efficient studies will be achieved. Extensive simulations were conducted and showed that, compared with trials where each participant receives one treatment, the proposed participants reusing RAR design can achieve comparable power with a smaller sample size and a shorter trial duration, especially when the accrual rate is low. The efficiency gain decreases as the accrual rate increases.

Keywords:

• Bayesian adaptive model
• Comparative effectiveness research
• Hierarchical models
• Response adaptive randomization

Additional information

Funding

This study was supported in part by an NIH Clinical and Translational Science Award (grant no. UL1TR002366) to the University of Kansas, and KUMC Biostatistics & Data Science Department, as well as The University of Kansas Cancer Center (grant no. P30 CA168524).