Optimizing Graphical Procedures for Multiplicity Control in a Confirmatory Clinical Trial via Deep Learning

TL;DR

This paper introduces a deep learning-enhanced optimization framework for graphical procedures in clinical trial multiplicity control, improving efficiency and power in hypothesis testing through neural network approximations.

Contribution

It proposes a novel deep learning-based method for optimizing graphical procedures, outperforming existing derivative-free algorithms in robustness and efficiency.

Findings

FNN-based approach offers better robustness and time efficiency.

Moderate power gain in large hypothesis settings.

Effective case study demonstration.

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.