The Bayesian approach is being used increasingly in medical research. The flexibility of the Bayesian approach allows for building designs of clinical trials that have good properties of any desired sort. Examples include maximizing effective treatment of patients in the trial, maximizing information about the slope of a dose–response curve, minimizing costs, minimizing the number of patients treated, minimizing the length of the trial and combinations of these desiderata. They also include standard frequentist operating characteristics when these are important considerations. Posterior probabilities are updated via Bayes’ theorem on the basis of accumulating data. These are used to effect modifications of the trial’s course, including stopping accrual, extending accrual beyond that originally planned, dropping treatment arms, adding arms, etc. An important aspect of the approach I advocate is modeling the relationship between a trial’s primary endpoint and early indications of patient performance—auxiliary endpoints. This has several highly desirable consequences. One is that it improves the efficiency of adaptive trials because information is available sooner than otherwise.