Liu G Frank
Merck & Co., Inc., North Wales, PA, USA.
Pharm Stat. 2018 Feb;17(1):61-73. doi: 10.1002/pst.1836. Epub 2017 Nov 10.
Traditionally, noninferiority hypotheses have been tested using a frequentist method with a fixed margin. Given that information for the control group is often available from previous studies, it is interesting to consider a Bayesian approach in which information is "borrowed" for the control group to improve efficiency. However, construction of an appropriate informative prior can be challenging. In this paper, we consider a hybrid Bayesian approach for testing noninferiority hypotheses in studies with a binary endpoint. To account for heterogeneity between the historical information and the current trial for the control group, a dynamic P value-based power prior parameter is proposed to adjust the amount of information borrowed from the historical data. This approach extends the simple test-then-pool method to allow a continuous discounting power parameter. An adjusted α level is also proposed to better control the type I error. Simulations are conducted to investigate the performance of the proposed method and to make comparisons with other methods including test-then-pool and hierarchical modeling. The methods are illustrated with data from vaccine clinical trials.
传统上，非劣效性假设是使用具有固定界值的频率论方法进行检验的。鉴于对照组的信息通常可从先前的研究中获得，考虑一种贝叶斯方法是很有意思的，在这种方法中，为对照组“借用”信息以提高效率。然而，构建合适的信息先验可能具有挑战性。在本文中，我们考虑一种混合贝叶斯方法，用于在具有二元终点的研究中检验非劣效性假设。为了考虑历史信息与对照组当前试验之间的异质性，提出了一种基于动态P值的效能先验参数，以调整从历史数据中借用的信息量。这种方法扩展了简单的先检验后合并方法，以允许连续的折扣效能参数。还提出了一个调整后的α水平，以更好地控制I型错误。进行了模拟以研究所提出方法的性能，并与其他方法（包括先检验后合并和分层建模）进行比较。用疫苗临床试验的数据对这些方法进行了说明。
