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.
翻译:在确认临床试验中,有人提议使用简单的迭代图形方法,用加权的Bonferroni型程序来构建和进行交叉假设测试,以控制强烈意义上的I型错误。鉴于第二阶段的研究结果或其他先前知识,通常主要感兴趣的是找到最佳图表,在未来第三阶段的研究中最大限度地发挥某种客观功能。在本条中,我们评价两种现有的无衍生物限制方法的性能,并进一步提议一个深层次的学习强化优化框架。我们的方法通过饲料前神经网络(FNN)在数字上接近目标功能,然后利用现有的梯度信息进行优化。我们可以加以限制,以便在优化其他特征的同时保持测试程序的某些特征。模拟研究表明,我们的FNNN方法比某些现有的无衍生物限制优化算法在稳健性和时间效率之间有更好的平衡。与传统的随机搜索方法相比,当假设数量相对较大时,我们的优化者有适度的多重调整能力收益。我们进一步将它应用于案例研究,以说明如何在特定目标研究方面优化多种测试程序。