Dynamic Information Borrowing From External Data in Clinical Trials: The Elastic Commensurate Prior Approach

Integrating external data into a clinical trial can introduce systematic bias in estimates and inflate the study's type I error due to differences in study design and enrollment criteria. Existing prior designs for information borrowing lack the ability to dynamically adjust the weight based on the similarity between concurrent and external data. To address this challenge, we thereby introduce a novel method called the elastic commensurate prior (ECP), which combines the commensurate prior with the elastic prior method. By dynamically adjusting the weight of external data using a measure of congruence, this method demonstrates strong performance in maintaining power while providing adequate type I error control across different scenarios, including congruence, approximate congruence, and incongruence between external and concurrent data. Compared to existing methods such as the modified power prior, meta-analytic-predictive (MAP) prior, robust MAP prior, non-informative prior, and fully informative prior, the ECP method is flexible and performs well across all settings. Furthermore, our method also allows for the integration of covariates in estimating data congruence for dynamic information borrowing, achieving both strong performance in power and adequate control of type I error. Overall, the ECP represents a promising option for leveraging external data in clinical trials, reducing costs by decreasing the sample size requirement, and thereby accelerating research and drug development timelines.