How Suvoda RTSM Automates Adaptive Enrollment In Basket Trials

By Erica Jonas, VP, Services Delivery

Basket trials test a single targeted therapy across multiple tumor types grouped by shared genetic mutation rather than cancer location, allowing sponsors to reach efficacy signals faster and with fewer patients. But this design introduces a persistent operational challenge: as some tumor-type cohorts respond well and others show early signs of futility, study teams must continuously reassess which cohorts should stay open and which should close. Bayesian Futility Analysis offers a statistically rigorous way to make that call in real time, but manually calculating it at every enrollment event creates delays that can leave patients enrolling into cohorts already trending toward failure.

This piece explores how automating the connection between trial data and statistical algorithms allows cohort enrollment decisions to be made continuously and accurately, without manual intervention, while still preserving full override capability for study teams. It also looks at where the underlying methodology is heading, including information borrowing across cohorts and more frequent interim assessments. For sponsors running or planning adaptive basket trials, closing the gap between statistical sophistication and operational execution is essential to protecting patients and keeping trials moving efficiently. Read on to see how that gap can be closed.