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Bayesian Adaptive Design — SCE Medical Oncology MCQ

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HardClinical Trials & StatisticsBayesian Adaptive DesignSCE Medical Oncology

A rare-cancer platform trial specifies an external-data prior, updates it after each cohort and drops an arm when the posterior probability of a clinically worthwhile effect falls below a prespecified boundary. Which statement describes the analysis?

Educational content. Not a substitute for clinical judgement or local policy.

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Correct answer: A — It combines a prior with the likelihood to update posterior decision probabilities

Explanation lettering: E = shown as A · A = shown as E

E is correct. Bayesian inference combines the prior distribution with the observed-data likelihood to form a posterior distribution, from which prespecified efficacy or futility probabilities can drive adaptation. A frequentist p value is not the Bayesian estimand. Operating characteristics, including false-positive risk, still require simulation and calibration; a posterior threshold does not guarantee them automatically. Adaptations must be prospectively specified to protect interpretability, and a credible interval has a direct posterior probability interpretation that differs from a frequentist confidence interval. The method can borrow information efficiently in small populations, but sensitivity to the prior must be examined.

Reference: EMA concept paper on Bayesian methods in clinical development: https://www.ema.europa.eu/en/documents/scientific-guideline/concept-paper-development-reflection-paper-use-bayesian-methods-clinical-development_en.pdf