Comparing Bayesian Dynamic Borrowing and Synthetic Control Methods: Apples to Oranges?

Nicole Cizauskas; Foteini Strimenopoulou; Svetlana S. Cherlin; James M. S. Wason

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arXiv · 2601.23021

Comparing Bayesian Dynamic Borrowing and Synthetic Control Methods: Apples to Oranges?

Nicole Cizauskas·Foteini Strimenopoulou·Svetlana S. Cherlin·James M. S. Wason

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Abstract

Bayesian dynamic borrowing (BDB) and synthetic control methods (SCM) are strategies to incorporate historical data in clinical trial design when control recruitment is a challenge. While both methods aim to improve efficiency, they differ in conceptual approach: BDB adjusts borrowing based on prior-data conflict, whereas SCM synthesizes individual-level control outcomes. This study aims to conduct a comparison BDB (using meta-analytic predictive prior (MAP)) and SCM using a case study of Pediatric Atopic Dermatitis. Six historical randomised control trials were selected for use in both the creation of the MAP prior and synthetic control arm. BDB MAP prior produced a mean response rate (historical control response probability) of 0.22 (posterior 95% credible interval: 0.11-0.36) (0.22 and 0.11-0.37 robustified), a power of 0.58, a type 1 error rate of 0.03, bias of 0.0007 (0.0008 robustified), a coverage of 0.96 (0.94 robustified) and prior ESS of 8.9 (3.32 robustified). SCM produced a mean response rate of 0.26 (95% confidence interval: 0.17-0.36), a power of 0.75, type 1 error rate of 0.013, bias of -0.0002, and coverage of 0.97; when adjusted to match the BDB ESS, SCM reported a similar mean response rate (0.27 and 0.13-0.46). a lower power (0.54), type 1 error rate (0.002) and bias(-0.0006), and the same coverage rate (0.97) to the initial SCM results. For this case study, the sample size adjusted SCM model produced a relatively similar power and type 1 error rate to the BDB MAP prior method. However, direct comparisons between these two methods fail to capture the true relationship due to how the data is handled differently and the purpose of each method. Method selection should consider trial-specific constraints, prior-data heterogeneity, and the desired balance between efficiency and robustness.

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Nicole Cizauskas, Foteini Strimenopoulou, Svetlana S. Cherlin, James M. S. Wason. 2026-01-30. Comparing Bayesian Dynamic Borrowing and Synthetic Control Methods: Apples to Oranges?. https://arxiv.org/abs/2601.23021

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