Description

In this QCast episode, join co-hosts Jullia and Tom as they unpack the Bayesian Optimal Interval, or BOIN, design for early-phase oncology studies. You'll hear how BOIN blends Bayesian reasoning with simple decision rules, why regulators now call it 'fit for purpose,' and how sponsors are using it to speed up dose-finding while keeping patient safety front and centre.

Key Takeaways

• BOIN design is a model-assisted Bayesian dose-escalation method with pre-defined escalation, de-escalation and safety rules.
• Core concepts involve setting a target toxicity rate, calculating helper rates at 60% and 140%, deriving decision limits via Bayesian posteriors and including a 95% safety stop.
• Compared to the 3+3 method, BOIN locates the true MTD more accurately, allows flexible cohort sizes, and runs off a simple printed decision table.
• Operational essentials include embedding the decision table in your protocol, aligning toxicity assessment windows (or using time-to-event BOIN), training site teams with examples and running light simulations.
• Common pitfalls are choosing an unrealistic toxicity target, treating BOIN as 'set-and-forget,' and adding extensions without recalculating the decision boundaries.

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About Quanticate

Founded in 1994, Quanticate is a biometrics-focused CRO with over 30 years' experience delivering expert clinical and post-marketing data services worldwide. Specialising in the collection, management, standardisation, analysis, and reporting of data, we support pharma and biotech companies globally with high-quality solutions for clinical development. We specialise in:

• Clinical Data Capture & Management
• Biostatistics & Statistical Consultancy
• Statistical Programming & PK/PD Analysis
• Medical Writing & Pharmacovigilance
• Regulatory & Submission Support
• AI & Automation
• Post-Marketing Safety & Real-World Data

Quanticate is committed to delivering expertise and solutions that drive the success of clinical development worldwide.