Response-Adaptive Designs for Clinical Trials: Simultaneous Learning from Multiple Patients

European Journal of Operational Research 248 (2016) 619–633.

Posted: 10 Aug 2012 Last revised: 1 Jan 2016

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 21, 2015

Abstract

Clinical trials have traditionally followed a fixed design, in which randomization probabilities of patients to various treatments remains fixed throughout the trial and specified in the protocol. The primary goal of this static design is to learn about the efficacy of treatments. Response-adaptive designs, on the other hand, allow clinicians to use the learning about treatment effectiveness to dynamically adjust randomization probabilities of patients to various treatments as the trial progresses. An ideal adaptive design is one where patients are treated as effectively as possible without sacrificing the potential learning or compromising the integrity of the trial. We propose such a design, termed Jointly Adaptive, that uses forward-looking algorithms to fully exploit learning from multiple patients simultaneously. Compared to the best existing implementable adaptive design that employs a multiarmed bandit framework in a setting where multiple patients arrive sequentially, we show that our proposed design improves health outcomes of patients in the trial by up to 8.6%, in expectation, under a set of considered scenarios. Further, we demonstrate our design's effectiveness using data from a recently conducted stent trial. This paper also adds to the general understanding of such models by showing the value and nature of improvements over heuristic solutions for problems with short delays in observing patient outcomes. We do this by showing the relative performance of these schemes for maximum expected patient health and maximum expected learning objectives, and by demonstrating the value of a restricted-optimal-policy approximation in a practical example.

Keywords: OR in health services; Adaptive clinical trials; Markov decision process; Bayesian learning; Stents.

JEL Classification: C11, C44, I18

Suggested Citation

Ahuja, Vishal and Birge, John R., Response-Adaptive Designs for Clinical Trials: Simultaneous Learning from Multiple Patients (June 21, 2015). European Journal of Operational Research 248 (2016) 619–633.. Available at SSRN: https://ssrn.com/abstract=2126906 or http://dx.doi.org/10.2139/ssrn.2126906

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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