We propose to use Bayesian optimization (BO) to improve the efficiency of the design selection process in clinical trials. BO is a method to optimize expensive black-box functions, by using a regression as a surrogate to guide the search. In clinical trials, planning test procedures and sample sizes is a crucial task. A common goal is to maximize the test power, given a set of treatments, corresponding effect sizes, and a total number of samples. From a wide range of possible designs we aim to select the best one in a short time to allow quick decisions. The standard approach to simulate the power for each single design can become too time-consuming. When the number of possible designs becomes very large, either large computational resources are required or an exhaustive exploration of all possible designs takes too long. Here, we propose to use BO to quickly find a clinical trial design with high power from a large number of candidate designs. We demonstrate the effectiveness of our approach by optimizing the power of adaptive seamless designs for different sets of treatment effect sizes. Comparing BO with an exhaustive evaluation of all candidate designs shows that BO finds competitive designs in a fraction of the time.

翻译：我们提议利用贝叶斯优化(BO)来提高临床试验中设计选择过程的效率。BO是一种优化昂贵黑盒功能的方法,它利用回归作为替代工具来引导搜索。在临床试验中,规划测试程序和样本大小是一项关键任务。一个共同的目标是根据一套治疗方法、相应的效果大小和样本总数,使测试能力最大化。从一系列可能的设计中,我们的目标是在短时间内选择最佳的,以便作出迅速的决定。模拟每种设计的能力的标准方法可能变得过于耗时。当可能的设计量非常大时,要么需要大量的计算资源,要么需要对所有可能的设计进行彻底的探索,耗时过长。在这里,我们提议利用BO迅速找到具有大量候选设计高功率的临床试验设计。我们通过优化不同治疗效果大小的适应性无缝设计的能力来证明我们的方法的有效性。将BO与所有候选设计的全面评估方法相匹配,表明BO在一定的时间里找到竞争性的设计。