Surrogate endpoints are commonly used in clinical trials when the true endpoint is difficult to achieve. R.L. Prentice (1986) proposed the definition and operational criteria for the validity of a surrogate endpoint within the statistical framework of hypothesis testing. The main criterion requires acceptance of the null hypothesis of conditional independence of a true endpoint and the treatment given the surrogate endpoint. In this current research, we evaluate the validity of the surrogate endpoint using the equivalence hypothesis under a Bayesian framework. We consider the true and the surrogate endpoints to be normally distributed. We test the equivalence hypothesis by computing Bayes factors under various sets of priors. This method for evaluating the surrogate endpoint is also applied to a real dataset.