The design of a small Bayesian hybrid-controlled trial utilizing time-to-event data from six published sources that used best alternative care to treat liver-predominant metastasis from primary ocular melanoma.

Background: Time-to-event endpoints, such as progression-free survival (PFS), are used to evaluate clinical activity in early-phase RCTs in cancer research. Because the power to detect a reduction in the hazard rate depends on the number of events observed, recruiting enough patients with a rare cancer can be difficult. Bayesian designs that leverage historical control data to augment the control arm are sought in rare cancers because there is a practical and ethical willingness to trade modest increases in bias and Type 1 error, particularly under prior-new data conflict, for meaningful reductions in variance and improved feasibility. Purpose: The purpose is to provide a real-world, from beginning to end application to design a time-to-event hybrid-controlled trial for a rare cancer with best alternative care (BAC) as the control that uses robust borrowing from six BAC PFS data sources and explicitly evaluates the operating characteristics across degrees of prior-new data conflict. Methods: A simulation-based approach for Bayesian sample size determination was used and calibrated to possess good frequentist Type 1 and 2 error properties. Results: A Bayesian hybrid-controlled RCT resulted in a 28% reduction in the number of participants needed to detect a 50% reduction in the hazard rate compared with a traditional frequentist design with the same power. Conclusions: Utilizing historical BAC PFS data from 309 patients with liver-predominant metastasis from primary ocular melanoma can enhance trial efficiency by assigning fewer subjects to the control arm and potentially lead to the earlier availability of effective therapies to patients with this rare cancer.