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

Sofía S. Villar, James Wason, Jack Bowden

Research output: Contribution to journalArticle (Academic Journal)

13 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)969-978
Number of pages10
JournalBiometrics
Volume71
Issue number4
DOIs
Publication statusPublished - 1 Dec 2015

Keywords

  • Bayesian adaptive designs
  • Clinical trials
  • Gittins index
  • Multi-armed bandit
  • Sequential allocation

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