Optimal Stopping of Adaptive Dose-Finding Trials

Author:

Nasrollahzadeh Amir Ali1ORCID,Khademi Amin2ORCID

Affiliation:

1. Department of Mechanical and Aerospace Engineering, University of California, Davis, Davis, California 95616;

2. Department of Industrial Engineering, Clemson University, Clemson, South Carolina 29634

Abstract

The primary objective of this paper is to develop computationally efficient methods for optimal stopping of an adaptive Phase II dose-finding clinical trial, where the decision maker may terminate the trial for efficacy or abandon it as a result of futility. We develop two solution methods and compare them in terms of computational time and several performance metrics such as the probability of correct stopping decision. One proposed method is an application of the one-step look-ahead policy to this problem. The second proposal builds a diffusion approximation to the state variable in the continuous regime and approximates the trial’s stopping time by optimal stopping of a diffusion process. The secondary objective of the paper is to compare these methods on different dose-response curves, particularly when the true dose-response curve has no significant advantage over a placebo. Our results, which include a real clinical trial case study, show that look-ahead policies perform poorly in terms of the probability of correct decision in this setting, whereas our diffusion approximation method provides robust solutions.

Publisher

Institute for Operations Research and the Management Sciences (INFORMS)

Subject

Marketing,Management Science and Operations Research,Modeling and Simulation,Business and International Management

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Sequential Learning with a Similarity Selection Index;Operations Research;2023-05-17

2. Adaptive Design of Personalized Dose-Finding Clinical Trials;Service Science;2022-12

3. Dynamic Programming for Response-Adaptive Dose-Finding Clinical Trials;INFORMS Journal on Computing;2021-10-21

4. Optimal Learning and Optimal Design;Springer Series in Supply Chain Management;2012-02-24

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