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.

中文翻译：

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使用加权平均的动态历史数据借用

在许多临床试验中，特别是在罕见病或特定人群（如儿科）的试验中，纳入历史数据以增加评估实验药物治疗效果的能力具有重要意义。在实践中，历史数据和当前数据可能不一致，借用历史数据往往与偏差和 1 类错误率膨胀有关。历史数据借用方法如何将Type-1错误率通胀控制在足够的水平，同时保持足够的权力仍然是一个挑战。为了解决这个问题，动态历史借用方法可以在历史数据与当前数据相似时更多地借用历史数据，否则更少。本文提出使用历史和当前控制数据的加权平均值，权重被设置为最佳权重的近似值，以最小化治疗效果估计中的均方误差。与选定的现有方法相比，所提出的方法通过模拟研究显示出减少的偏差、强大的功率增益和更好的控制类型 1 错误率膨胀。所提出的方法能够利用公共领域中所有可能的历史数据，并且可以通过跳过某些现有方法中对外部专家输入的需求来轻松使用。