Fakultät Statistik, Technische Universität Dortmund, Dortmund, Germany.
Institut für Medizinische Statistik, Universitätsmedizin Göttingen, Göttingen, Germany.
Biom J. 2022 Jun;64(5):948-963. doi: 10.1002/bimj.202000389. Epub 2022 Feb 25.
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.
我们建议使用贝叶斯优化（BO）来提高临床试验设计选择过程的效率。BO 是一种通过回归来指导搜索，从而优化昂贵的黑盒函数的方法。在临床试验中，规划测试程序和样本量是一项关键任务。一个常见的目标是在给定一组治疗方法、相应的效果大小和总样本数量的情况下，最大化测试功效。从广泛的可能设计中，我们旨在在短时间内选择最佳设计，以便快速做出决策。对于每个单一设计模拟功效的标准方法可能会变得过于耗时。当可能的设计数量非常大时，要么需要大量的计算资源，要么对所有可能的设计进行详尽的探索需要花费太长时间。在这里，我们建议使用 BO 从大量候选设计中快速找到具有高功效的临床试验设计。我们通过优化不同治疗效果大小集的自适应无缝设计的功效来证明我们方法的有效性。将 BO 与对所有候选设计的详尽评估进行比较表明，BO 可以在一小部分时间内找到具有竞争力的设计。
