DBMS: dynamic borrowing method for frequentist hybrid control designs based on historical-current data similarity.

Abstract

Information borrowing from historical data is gaining increasing attention in clinical trials for rare and pediatric diseases, where small sample sizes may lead to insufficient statistical power for confirming efficacy. While Bayesian information borrowing methods are well established, recent frequentist approaches, such as the test-then-pool and equivalence-based test-then-pool methods, have been proposed to determine whether historical data should be incorporated into statistical hypothesis testing. Depending on the outcome of these hypothesis tests, historical data may or may not be utilized. This paper introduces a dynamic borrowing method for leveraging historical information based on the similarity between current and historical data. Similar to Bayesian dynamic borrowing, our proposed method adjusts the degree of information borrowing dynamically, ranging from 0 to 100 %. We present two approaches to measure similarity: one using the density function of the t-distribution and the other employing a logistic function. The performance of the proposed methods is evaluated through Monte Carlo simulations. Additionally, we demonstrate the utility of dynamic information borrowing by reanalyzing data from an actual clinical trial.