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A novel statistical test for treatment differences in clinical trials using a response‐adaptive forward‐looking Gittins Index Rule

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  • Helen Yvette Barnett
  • Sofía S. Villar
  • Helena Geys
  • Thomas Jaki

Abstract

The most common objective for response‐adaptive clinical trials is to seek to ensure that patients within a trial have a high chance of receiving the best treatment available by altering the chance of allocation on the basis of accumulating data. Approaches that yield good patient benefit properties suffer from low power from a frequentist perspective when testing for a treatment difference at the end of the study due to the high imbalance in treatment allocations. In this work we develop an alternative pairwise test for treatment difference on the basis of allocation probabilities of the covariate‐adjusted response‐adaptive randomization with forward‐looking Gittins Index (CARA‐FLGI) Rule for binary responses. The performance of the novel test is evaluated in simulations for two‐armed studies and then its applications to multiarmed studies are illustrated. The proposed test has markedly improved power over the traditional Fisher exact test when this class of nonmyopic response adaptation is used. We also find that the test's power is close to the power of a Fisher exact test under equal randomization.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:biomet:v:79:y:2023:i:1:p:86-97
    DOI: 10.1111/biom.13581
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    References listed on IDEAS

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    1. 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.
    2. Sofía S. Villar & William F. Rosenberger, 2018. "Covariate†adjusted response†adaptive randomization for multi†arm clinical trials using a modified forward looking Gittins index rule," Biometrics, The International Biometric Society, vol. 74(1), pages 49-57, March.
    3. Simon, Richard & Simon, Noah Robin, 2011. "Using randomization tests to preserve type I error with response adaptive and covariate adaptive randomization," Statistics & Probability Letters, Elsevier, vol. 81(7), pages 767-772, July.
    4. repec:bla:biomet:v:71:y:2015:i:4:p:969-978 is not listed on IDEAS
    5. 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.
    6. Adam L. Smith & Sofía S. Villar, 2018. "Bayesian adaptive bandit-based designs using the Gittins index for multi-armed trials with normally distributed endpoints," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(6), pages 1052-1076, April.
    7. Zhiwei Zhang & Meijuan Li & Min Lin & Guoxing Soon & Tom Greene & Changyu Shen, 2017. "Subgroup selection in adaptive signature designs of confirmatory clinical trials," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(2), pages 345-361, February.
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