Improving Adaptive Seamless Designs through Bayesian optimization

TL;DR

This paper introduces Bayesian optimization to efficiently select optimal adaptive seamless designs in clinical trials, significantly reducing computation time while maintaining high test power.

Contribution

It applies Bayesian optimization to clinical trial design selection, enabling rapid identification of high-power designs from large candidate sets.

Findings

BO finds competitive designs faster than exhaustive search.

BO reduces computational resources needed for design optimization.

Effective in optimizing adaptive seamless trial designs.

Abstract

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.