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Response-adaptive randomization for multi-arm clinical trials using the forward looking Gittins index rule

Lookup NU author(s): Professor James WasonORCiD



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.

Publication metadata

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


DOI: 10.1111/biom.12337

PubMed id: 26098023


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