Yu Zhenning, Ramakrishnan Viswanathan, Meinzer Caitlyn
a Graduate Research Assistant, Data Coordination Unit, Department of Public Health Sciences , Medical University of South Carolina , Charleston , SC , USA.
b Department of Public Health Sciences , Medical University of South Carolina , Charleston , SC , USA.
J Biopharm Stat. 2019;29(2):306-317. doi: 10.1080/10543406.2019.1577682. Epub 2019 Feb 14.
Multi-arm multi-stage designs, in which multiple active treatments are compared to a control and accumulated information from interim data are used to add or remove arms from the trial, may reduce development costs and shorten the drug development timeline. As such, this adaptive update is a natural complement to Bayesian methodology in which the prior clinical belief is sequentially updated using the observed probability of success. Simulation is often required for planning clinical trials to accommodate the complexity of the design and to optimize key design characteristics. This paper addresses two key limiting factors in simulations, namely the computational burden and the time needed to obtain results. We first introduce a generic process for simulating Bayesian multi-arm multi-stage designs with binary endpoints. Then, to address the computational burden and time, we optimize the method for calculating the posterior probability and posterior predictive probability of success.
多臂多阶段设计是将多种活性治疗与一种对照进行比较，并利用中期数据积累的信息来增加或减少试验中的臂数，这种设计可能会降低研发成本并缩短药物研发时间线。因此，这种适应性更新是贝叶斯方法的自然补充，在贝叶斯方法中，先前的临床信念会根据观察到的成功概率进行顺序更新。规划临床试验时通常需要进行模拟，以适应设计的复杂性并优化关键设计特征。本文探讨了模拟中的两个关键限制因素，即计算负担和获得结果所需的时间。我们首先介绍一种用于模拟具有二元终点的贝叶斯多臂多阶段设计的通用过程。然后，为了解决计算负担和时间问题，我们优化了计算成功的后验概率和后验预测概率的方法。
