It is expected to develop new drug more efficiently by incorporating historical data into the current study data. Borrowing historical data which is sufficiently similar to the current data allows increasing power and improving the accuracy of the estimated treatment effect. On the other hand, if the historical data is not similar to the current data, there is a potential for bias and inflated type I error rate. Power prior and hierarchical model are widely known as the Bayesian approaches with borrowing strength from historical information. They have the advantage of deciding the amount of historical information continuously depending on the similarity between historical data and current data. Our goal is to introduce power prior and hierarchical model while showing some examples, and provide a review of points to keep in mind when these approaches are used in the clinical trials.