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Abstract:
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Clinical trials often encounter recruitment and retention challenges. Borrowing historical data may alleviate these challenges by reducing the number of participants and shortening trial duration while increasing statistical efficiency. However, naively borrowing information to the current trial can result in biased estimates and inflated false discoveries. We proposed a machine learning-based dynamic borrowing approach – Regularized Bayes (RB) to ensure efficient borrowing via minimizing mean square error (MSE) to optimize the balance between bias and uncertainty. A regularization term was used to link the current study with external data to dynamically calibrate the degree of borrowing. The final estimate based on RB is anticipated to improve the MSE based on independent analysis (IND) of the current study data (i.e., no borrowing). The simulation results showed that the RB had similar or better adaptivity of discounting historical information with higher between-trial heterogeneity, leading up to 55% lower MSE relative to IND. In addition, the RB showed an improved true positive rate (TPR) compared to both IND and meta-analytic prior (MAP), at about 15% to 30%.
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