In the dynamic world of oncology drug development, the complexity of dose-finding studies increases substantially when multiple disease types are evaluated within a single trial. The heterogeneity between cancer types poses a critical challenge: how can we design efficient dose-escalation procedures that account for patient differences across indications particularly when one indication recruits more quickly than the other?

A new approach, cautious iBOIN (ciBOIN), offers a compelling answer. Built on the foundation of the Bayesian Optimal Interval (BOIN) design and its variant with informative priors (iBOIN), ciBOIN introduces a prudent method for borrowing strength from common cancer types that recruit faster to rarer types with slower recruitment while maintaining separate maximum tolerated dose (MTD) estimation for each cancer type.

The dose-escalation dilemma in multi-cohort trials

Traditional dose-escalation designs often face a trade-off between safety and efficiency. When trials pool data across disease types, they risk obscuring differences in toxicity profiles.

On the other hand, treating each type entirely independently can lead to missed opportunities to leverage valuable information.

Enter ciBOIN: A pragmatic compromise

The ciBOIN method was developed as a compromise between pooling disease types and separate dose-escalation. It allows dose-escalation decisions in the slower-recruiting disease type to be cautiously influenced by data from the faster-recruiting one. The design is particularly appealing in trials where each disease type may require a distinct MTD estimation due to differing patient profiles.

Through extensive simulations, ciBOIN was compared against separate dose-escalation using BOIN over a range of scenarios. The assessed scenarios and results can be classified in three categories:

• Same toxicity in both disease types: ciBOIN leads to similar or slightly better MTD detection rates with less patients overdosed and a lower DLT rate compared to a separate dose-escalation.
• Higher toxicity in the common disease type: ciBOIN underestimates the MTD for the rare type but achieves improved safety, reducing the number of patients exposed to overly toxic doses and lowering the overall dose-limiting toxicity (DLT) rate compared to a separate dose-escalation.
• Higher toxicity in the rare disease type: Here, ciBOIN again underestimates the MTD a bit, this time in the common disease type, but again with reduced overdosing rates.

Overall, ciBOIN results in smaller trial sizes. The highest reduction (~3 patients) with ciBOIN compared to separate dose escalation was observed in the highest dose-toxicity profile.

A balanced path forward

The findings support ciBOIN as a viable compromise between full pooling and strict separation. It ensures that dose recommendations are never too aggressive, thereby safeguarding patient safety while still achieving gains in operational efficiency.

Notably, ciBOIN enables a nuanced strategy: one that adapts to the heterogeneity of real-world oncology trials without overcomplicating implementation. For sponsors and statisticians navigating increasingly complex pipelines, this approach may offer a timely and practical innovation.

Looking ahead

As oncology trials continue to evolve toward platform and umbrella designs, methods like ciBOIN will be instrumental in ensuring both flexibility and rigor. Future work may explore extending the framework to accommodate more than two cohorts or using other approaches than BOIN and iBOIN.

Ultimately, ciBOIN exemplifies how thoughtful design choices, informed by Bayesian thinking and tempered by clinical caution, can help meet the dual mandate of safety and speed in early-phase drug development.

Interested in learning more?

Martin Kappler, along with Yuan Ji from the University of Chicago, will present “ciBOIN — A Bayesian-Informed Dose-Escalation Design for Multi-Cohort Oncology Trials with Potentially Varying Maximum Tolerated Doses” at the 46th Annual Conference of the International Society for Clinical Biostatistics (ISCB) on August 24–28, 2025, in Basel, Switzerland.

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