  Information borrowing from historical data is gaining attention in clinical
trials of rare and pediatric diseases, where statistical power may be
insufficient for confirmation of efficacy if the sample size is small. Although
Bayesian information borrowing methods are well established, test-then-pool and
equivalence-based test-then-pool methods have recently been proposed as
frequentist methods to determine whether historical data should be used for
statistical hypothesis testing. Depending on the results of the hypothesis
testing, historical data may not be usable. This paper proposes a dynamic
borrowing method for historical information based on the similarity between
current and historical data. In our proposed method of dynamic information
borrowing, as in Bayesian dynamic borrowing, the amount of borrowing ranges
from 0% to 100%. We propose two methods using the density function of the
t-distribution and a logistic function as a similarity measure. We evaluate the
performance of the proposed methods through Monte Carlo simulations. We
demonstrate the usefulness of borrowing information by reanalyzing actual
clinical trial data.
