An Approximation Approach for Response Adaptive Clinical Trial Design

54 Pages Posted: 1 Aug 2018 Last revised: 15 Jun 2019

See all articles by Vishal Ahuja

Vishal Ahuja

Southern Methodist University (SMU) - Information Technology and Operations Management Department (ITOM)

John R. Birge

University of Chicago - Booth School of Business

Date Written: June 13, 2019

Abstract

Multi-armed bandit (MAB) problems, typically modeled as Markov decision processes (MDPs), exemplify the learning vs. earning tradeoff. An area that has motivated theoretical research in MAB designs is the study of clinical trials, where the application of such designs has the potential to significantly improve patient outcomes. However, for many practical problems of interest, the state space is intractably large, rendering exact approaches to solving MDPs impractical. In particular, settings that require multiple simultaneous allocations lead to an expanded state and action-outcome space, necessitating the use of approximation approaches. We propose a novel approximation approach that combines the strengths of multiple methods: grid-based state discretization, value function approximation methods, and techniques for a computationally efficient implementation. The hallmark of our approach is the accurate approximation of the value function that combines linear interpolation with bounds on interpolated value and the addition of a learning component to the objective function. Computational analysis on relevant datasets shows that our approach outperforms existing heuristics (e.g. greedy and upper confidence bound family of algorithms) as well as a popular Lagrangian-based approximation method, where we find that the average regret improves by up to 58.3% (95% CI=58.1%-58.4%). A retrospective implementation on a recently conducted phase 3 clinical trial shows that our design could have reduced the number of failures by 17.03% (95% CI=17.02-17.05%) relative to the randomized control design used in that trial. Our proposed approach makes it practically feasible for trial administrators and regulators to implement Bayesian response-adaptive designs on large clinical trials with potential significant gains.

Keywords: Adaptive Clinical Trials, Markov Decision Process, Grid-Based Approximation, Adaptive Sampling, Approximate Dynamic Programming

Suggested Citation

Ahuja, Vishal and Birge, John R., An Approximation Approach for Response Adaptive Clinical Trial Design (June 13, 2019). SMU Cox School of Business Research Paper No. 18-26. Available at SSRN: https://ssrn.com/abstract=3212148 or http://dx.doi.org/10.2139/ssrn.3212148

Vishal Ahuja (Contact Author)

Southern Methodist University (SMU) - Information Technology and Operations Management Department (ITOM) ( email )

Dallas, TX 75275
United States

John R. Birge

University of Chicago - Booth School of Business ( email )

5807 S. Woodlawn Avenue
Chicago, IL 60637
United States

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