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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2015, The International Biometric Society. The Gittins index provides a well established, computationally attractive, optimal solution to a class of resource allocation problems known collectively as the multi-arm bandit problem. Its development was originally motivated by the problem of optimal patient allocation in multi-arm clinical trials. However, it has never been used in practice, possibly for the following reasons: (1) it is fully sequential, i.e., the endpoint must be observable soon after treating a patient, reducing the medical settings to which it is applicable; (2) it is completely deterministic and thus removes randomization from the trial, which would naturally protect against various sources of bias. We propose a novel implementation of the Gittins index rule that overcomes these difficulties, trading off a small deviation from optimality for a fully randomized, adaptive group allocation procedure which offers substantial improvements in terms of patient benefit, especially relevant for small populations. We report the operating characteristics of our approach compared to existing methods of adaptive randomization using a recently published trial as motivation.
Author(s): Villar SS, Wason J, Bowden J
Publication type: Article
Publication status: Published
Journal: Biometrics
Year: 2015
Volume: 71
Issue: 4
Pages: 969-978
Print publication date: 01/12/2015
Online publication date: 22/06/2015
Acceptance date: 01/04/2015
Date deposited: 25/01/2018
ISSN (print): 0006-341X
ISSN (electronic): 1541-0420
Publisher: Wiley-Blackwell
URL: https://doi.org/10.1111/biom.12337
DOI: 10.1111/biom.12337
PubMed id: 26098023
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