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Use of historical data and real-world evidence holds great potential toimprove the efficiency of clinical trials. One major challenge is how toeffectively borrow information from historical data while maintaining areasonable type I error. We propose the elastic prior approach to address thischallenge and achieve dynamic information borrowing. Unlike existingapproaches, this method proactively controls the behavior of dynamicinformation borrowing and type I errors by incorporating a well-known conceptof clinically meaningful difference through an elastic function, defined as amonotonic function of a congruence measure between historical data and trialdata. The elastic function is constructed to satisfy a set ofinformation-borrowing constraints prespecified by researchers or regulatoryagencies, such that the prior will borrow information when historical and trialdata are congruent, but refrain from information borrowing when historical andtrial data are incongruent. In doing so, the elastic prior improves power andreduces the risk of data dredging and bias. The elastic prior is informationborrowing consistent, i.e. asymptotically controls type I and II errors at thenominal values when historical data and trial data are not congruent, a uniquecharacteristics of the elastic prior approach. Our simulation study thatevaluates the finite sample characteristic confirms that, compared to existingmethods, the elastic prior has better type I error control and yieldscompetitive or higher power.

Elastic Priors to Dynamically Borrow Information from Historical Data in Clinical Trials