In a Bayesian Adaptive Design, prior knowledge about the treatment and the disorder is used to establish initial probabilities. As the trial progresses, new data is incorporated to update these probabilities. This process continues until sufficient evidence is gathered to reach a conclusion, whether that means continuing, modifying, or stopping the trial.
