Dynamic historical data borrowing using weighted average

In many clinical trials, especially trials in rare diseases or a certain population like paediatric, it is of great interest to incorporate historical data to increase power of evaluating the treatment effect of an experimental drug. In practice, historical data and current data may not be congruent, and borrowing historical data is often associated with bias and Type-1 error rate inflation. It remains a challenge for historical data borrowing methods to control Type-1 error rate inflation at an adequate level and maintain sufficient power at the same time. To address this issue, dynamic historical borrowing methods can borrow historical data more when historical data are similar to current data and less otherwise. This paper proposed to use a weighted average of historical and current control data, with the weight being set as an approximation to the optimal weight that minimizes the mean-squared errors in the treatment effect estimation. Comparing to selected existing methods, the proposed method showed reduced bias, robust gain in power and better control in Type-1 error rate inflation through simulation studies. The proposed method enables the utilization of all possible historical data in the public domain and is readily used by skipping the need for external expert input in some existing approaches.