Master protocols are reshaping oncology R&D, moving development from one drug in one tumor type toward integrated platforms that test multiple hypotheses at once. The designs are widely discussed. Far less attention goes to the biostatistical execution that determines whether they hold up under regulatory scrutiny.

This whitepaper draws on insights from senior biostatistical consulting leaders at Allucent to examine the gap between adaptive trial theory and real-world execution across basket, umbrella, and platform studies.

Inside the whitepaper:

• How to control multiplicity when evaluating multiple arms, subgroups, and biomarker cohorts in a single framework
• When shared control arms create correlated comparisons, and how to adjust for temporal drift in long-running platforms
• Where the FDA and EMA differ on Bayesian versus frequentist designs in confirmatory settings
• How Bayesian dynamic borrowing was applied across I-SPY 2, STAMPEDE, and ROAR
• Why operational infrastructure and governance, not statistical complexity, are the most common execution failures

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