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.

翻译：历史数据的信息借用方法在罕见病及儿科疾病的临床试验中日益受到关注，这类试验常因样本量不足而缺乏验证疗效的统计效力。尽管贝叶斯信息借用方法已得到充分发展，但近年来学者们提出基于"先检验再合并"与"等价性先检验再合并"的频率学派方法，以判断是否应将历史数据用于统计假设检验。然而，根据假设检验结果，历史数据可能无法被采用。本文提出一种基于当前数据与历史数据相似度的历史信息动态借用方法。与贝叶斯动态借用方法类似，我们提出的动态信息借用方法允许借用比例在0%至100%之间动态调整。具体而言，我们设计了两种基于相似度度量的方案：分别利用t分布密度函数和逻辑函数构建相似度指标。通过蒙特卡洛模拟评估所提方法的性能，并通过对实际临床试验数据的重新分析验证信息借用的有效性。