Bayesian dynamic borrowing (BDB) and synthetic control methods (SCM) are both used in clinical trial design when recruitment, retention, or allocation is a challenge. The performance of these approaches has not previously been directly compared due to differences in application, product, and measurement metrics. This study aims to conduct a comparison of power and type 1 error rates of 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. The R library RBesT was used to create a MAP prior and the R library Synthpop was used to create a synthetic control arm for the SCM. Power and type 1 error rate were used as comparison metrics. BDB produced a power of 0.580 and a type 1 error rate of 0.026. SCM produced a power of 0.641 and a type 1 error rate of 0.027. In this case study, the SCM model produced a higher power than the BDB method with a similar type 1 error rate. However, the decision to use SCM or BDB should come from the specific needs of the potential trial, since their power and type 1 error rate may differ on a case-by-case basis.

翻译：当临床试验面临招募、保留或分配困难时，贝叶斯动态借用（BDB）与合成控制方法（SCM）均被用于试验设计。由于应用场景、产品类型及评估指标存在差异，这些方法的性能此前尚未被直接比较。本研究旨在通过儿童特应性皮炎的案例，对BDB（采用荟萃分析预测先验（MAP））与SCM的检验功效和Ⅰ类错误率进行比较。研究选取了六项历史随机对照试验，同时用于构建MAP先验和合成对照组。采用R语言软件包RBesT构建MAP先验，并利用R语言软件包Synthpop为SCM构建合成对照组。以检验功效和Ⅰ类错误率作为比较指标。BDB方法的检验功效为0.580，Ⅰ类错误率为0.026；SCM方法的检验功效为0.641，Ⅰ类错误率为0.027。在本案例研究中，SCM模型相较于BDB方法展现出更高的检验功效，同时保持相近的Ⅰ类错误率。然而，选择使用SCM或BDB应基于具体试验的实际需求，因为其检验功效和Ⅰ类错误率可能因案例而异。