An Introduction to Bayesian Clinical Trial Design [Video]

Bayesian clinical trial design uses prior information alongside current trial data to update what is known about a treatment effect. This video explains how priors, posterior probability, predictive probability, adaptive decisions, and simulation fit together, and why the operational setup matters just as much as the statistical model.

WHAT THIS VIDEO COVERS
• How prior information and current trial data combine to form a posterior distribution
• The difference between posterior probability and predictive probability
• How Bayesian and frequentist approaches interpret evidence differently
• Where Bayesian methods can support dose decisions and other pre-specified adaptations
• How historical borrowing, sensitivity analysis, and conflict with external data are handled
• Why interim governance, data readiness, and simulation need to be planned before the trial begins

Bayesian methods can support flexible, probability-based decision-making, but those decisions still depend on clear assumptions, defined rules, and timely trial data. Simulation helps teams understand how the proposed design may behave across different scenarios before enrolment starts.

Quanticate’s statistical consultancy team supports sponsors with Bayesian clinical trial design strategy, prior justification, simulation planning, adaptive decision rules, historical data borrowing, and regulatory-ready statistical documentation. If you are considering whether Bayesian methods are appropriate for your study, or need support to plan, justify, and implement a Bayesian design, request a consultation today.