Increasingly, investigators are choosing to use Bayesian methods for the analysis of clinical trial data. Unlike classical statistical methods that treat model parameter values (such as treatment effects) as fixed, Bayesian methods view parameters as following a probability distribution. As we have written previously,1 by analyzing clinical trial data using Bayesian methods one can obtain quantities that may be of interest to clinicians, providers, and patients, such as the probability that a treatment effect is more or less than 0, that is, the probability that a treatment is effective.