Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.
Department of Epidemiology and Biostatistics, University of California, San Francisco, California, USA.
Pharm Stat. 2020 Sep;19(5):613-625. doi: 10.1002/pst.2018. Epub 2020 Mar 17.
Bayesian dynamic borrowing designs facilitate borrowing information from historical studies. Historical data, when perfectly commensurate with current data, have been shown to reduce the trial duration and the sample size, while inflation in the type I error and reduction in the power have been reported, when imperfectly commensurate. These results, however, were obtained without considering that Bayesian designs are calibrated to meet regulatory requirements in practice and even no-borrowing designs may use information from historical data in the calibration. The implicit borrowing of historical data suggests that imperfectly commensurate historical data may similarly impact no-borrowing designs negatively. We will provide a fair appraiser of Bayesian dynamic borrowing and no-borrowing designs. We used a published selective adaptive randomization design and real clinical trial setting and conducted simulation studies under varying degrees of imperfectly commensurate historical control scenarios. The type I error was inflated under the null scenario of no intervention effect, while larger inflation was noted with borrowing. The larger inflation in type I error under the null setting can be offset by the greater probability to stop early correctly under the alternative. Response rates were estimated more precisely and the average sample size was smaller with borrowing. The expected increase in bias with borrowing was noted, but was negligible. Using Bayesian dynamic borrowing designs may improve trial efficiency by stopping trials early correctly and reducing trial length at the small cost of inflated type I error.
贝叶斯动态借设计有助于从历史研究中借鉴信息。当历史数据与当前数据完全一致时，已证明可以缩短试验持续时间和样本量，但当不完全一致时，报告称会导致Ⅰ类错误膨胀和功效降低。然而，这些结果是在没有考虑到贝叶斯设计在实践中是为了满足监管要求而校准的情况下得出的，即使是不借设计也可能在校准中使用历史数据的信息。历史数据的隐性借用表明，不完全一致的历史数据可能同样对不借设计产生负面影响。我们将对贝叶斯动态借和不借设计进行公正评价。我们使用已发表的选择性适应性随机设计和真实临床试验设置，并在不同程度的不完全一致历史对照场景下进行模拟研究。在没有干预效果的零假设情况下，Ⅰ类错误会膨胀，而借设计会导致更大的膨胀。在替代假设下，早期正确停止试验的可能性更大，可以抵消零假设下Ⅰ类错误更大的膨胀。借设计可以更准确地估计反应率，并且平均样本量更小。虽然注意到了借设计会导致偏差增加，但可以忽略不计。使用贝叶斯动态借设计可以通过正确提前停止试验和缩短试验长度来提高试验效率，代价是略微增加Ⅰ类错误。
