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

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  • Sofía S. Villar
  • James Wason
  • Jack Bowden

Abstract

type="main" xml:lang="en"> 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.

Suggested Citation

  • Sofía S. Villar & James Wason & Jack Bowden, 2015. "Response-adaptive randomization for multi-arm clinical trials using the forward looking Gittins index rule," Biometrics, The International Biometric Society, vol. 71(4), pages 969-978, December.
    • Handle: RePEc:bla:biomet:v:71:y:2015:i:4:p:969-978
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    Citations

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    Cited by:

    1. Jennifer Proper & Thomas A. Murray, 2023. "An alternative metric for evaluating the potential patient benefit of response‐adaptive randomization procedures," Biometrics, The International Biometric Society, vol. 79(2), pages 1433-1445, June.
    2. Williamson, S. Faye & Jacko, Peter & Villar, Sofía S. & Jaki, Thomas, 2017. "A Bayesian adaptive design for clinical trials in rare diseases," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 136-153.
    3. Alisjahbana, Irene & Graur, Andrei & Lo, Irene & Kiremidjian, Anne, 2022. "Optimizing strategies for post-disaster reconstruction of school systems," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    4. Helen Yvette Barnett & Sofía S. Villar & Helena Geys & Thomas Jaki, 2023. "A novel statistical test for treatment differences in clinical trials using a response‐adaptive forward‐looking Gittins Index Rule," Biometrics, The International Biometric Society, vol. 79(1), pages 86-97, March.
    5. Williamson, S. Faye & Jacko, Peter & Jaki, Thomas, 2022. "Generalisations of a Bayesian decision-theoretic randomisation procedure and the impact of delayed responses," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
    6. Kasianova, Ksenia & Kelbert, Mark & Mozgunov, Pavel, 2021. "Response adaptive designs for Phase II trials with binary endpoint based on context-dependent information measures," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
    7. Pavel Mozgunov & Thomas Jaki, 2020. "An information theoretic approach for selecting arms in clinical trials," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(5), pages 1223-1247, December.
    8. Merrell, David & Chandereng, Thevaa & Park, Yeonhee, 2023. "A Markov decision process for response-adaptive randomization in clinical trials," Computational Statistics & Data Analysis, Elsevier, vol. 178(C).

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