Abstract

In confirmatory clinical trials, it has been proposed to use a simple iterative graphical approach to construct and perform intersection hypotheses tests with a weighted Bonferroni-type procedure to control Type I errors in the strong sense. Given Phase II study results or other prior knowledge, it is usually of main interest to find the optimal graph that maximizes a certain objective function in a future Phase III study. In this article, we evaluate the performance of two existing derivative-free constrained methods, and further propose a deep learning enhanced optimization framework. Our method numerically approximates the objective function via feedforward neural networks (FNNs) and then performs optimization with available gradient information. It can be constrained so that some features of the testing procedure are held fixed while optimizing over other features. Simulation studies show that our FNN-based approach has a better balance between robustness and time efficiency than some existing derivative-free constrained optimization algorithms. Compared to the traditional stochastic search method, our optimizer has moderate multiplicity adjusted power gain when the number of hypotheses is relatively large. We further apply it to a case study to illustrate how to optimize a multiple testing procedure with respect to a specific study objective.

摘要

在验证性临床试验中，建议使用简单的迭代图形方法来构建和执行交叉假设检验，并使用加权 Bonferroni 类型程序来控制严格意义上的 I 类错误。鉴于 II 期研究结果或其他先验知识，在未来的 III 期研究中找到最大化某个目标函数的最佳图通常是主要兴趣。在本文中，我们评估了两种现有的无导数约束方法的性能，并进一步提出了深度学习增强优化框架。我们的方法通过前馈神经网络 (FNN) 在数值上逼近目标函数，然后使用可用的梯度信息进行优化。可以对其进行约束，以使测试过程的某些特征保持固定，同时对其他特征进行优化。仿真研究表明，与一些现有的无导数约束优化算法相比，我们基于 FNN 的方法在鲁棒性和时间效率之间具有更好的平衡。与传统的随机搜索方法相比，当假设数量相对较大时，我们的优化器具有适度的多重调整功率增益。我们进一步将其应用于案例研究，以说明如何针对特定研究目标优化多重测试程序。与传统的随机搜索方法相比，当假设数量相对较大时，我们的优化器具有适度的多重调整功率增益。我们进一步将其应用于案例研究，以说明如何针对特定研究目标优化多重测试程序。与传统的随机搜索方法相比，当假设数量相对较大时，我们的优化器具有适度的多重调整功率增益。我们进一步将其应用于案例研究，以说明如何针对特定研究目标优化多重测试程序。