Use of historical data and real-world evidence holds great potential to improve the efficiency of clinical trials. One major challenge is to effectively borrow information from historical data while maintaining a reasonable type I error and minimal bias. We propose the elastic prior approach to address this challenge. Unlike existing approaches, this approach proactively controls the behavior of information borrowing and type I errors by incorporating a well-known concept of clinically significant difference through an elastic function, defined as a monotonic function of a congruence measure between historical data and trial data. The elastic function is constructed to satisfy a set of prespecified criteria such that the resulting prior will strongly borrow information when historical and trial data are congruent, but refrain from information borrowing when historical and trial data are incongruent. The elastic prior approach has a desirable property of being information borrowing consistent, that is, asymptotically controls type I error at the nominal value, no matter that historical data are congruent or not to the trial data. Our simulation study that evaluates the finite sample characteristic confirms that, compared to existing methods, the elastic prior has better type I error control and yields competitive or higher power. The proposed approach is applicable to binary, continuous, and survival endpoints.

中文翻译：

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从临床试验的历史数据中动态借用信息的弹性先验

使用历史数据和真实世界证据具有提高临床试验效率的巨大潜力。一个主要挑战是有效地从历史数据中借用信息，同时保持合理的 I 类错误和最小偏差。我们提出弹性先验方法来应对这一挑战。与现有方法不同，这种方法通过弹性函数结合众所周知的临床显着差异概念来主动控制信息借用和 I 类错误的行为，弹性函数定义为历史数据和试验数据之间一致性度量的单调函数。构建弹性函数以满足一组预先指定的标准，这样当历史数据和试验数据一致时，所产生的先验将强烈借用信息，但当历史数据和试验数据不一致时，不要借用信息。弹性先验方法具有信息借用一致的理想特性，即无论历史数据是否与试验数据一致，都将 I 类错误渐进地控制在标称值上。我们评估有限样本特征的模拟研究证实，与现有方法相比，弹性先验具有更好的 I 类错误控制，并产生具有竞争力或更高的功效。所提出的方法适用于二元、连续和生存终点。无论历史数据与试验数据是否一致。我们评估有限样本特征的模拟研究证实，与现有方法相比，弹性先验具有更好的 I 类错误控制，并产生具有竞争力或更高的功效。所提出的方法适用于二元、连续和生存终点。无论历史数据与试验数据是否一致。我们评估有限样本特征的模拟研究证实，与现有方法相比，弹性先验具有更好的 I 类错误控制，并产生具有竞争力或更高的功效。所提出的方法适用于二元、连续和生存终点。