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BBECT: Bandit -based Ethical Clinical Trials

Author

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  • Mohammed Shahid Abdulla

    (Indian Institute of Management Kozhikode)

  • L Ramprasath

    (Indian Institute of Management Kozhikode)

Abstract

An aim of Ethico-Optimal clinical trials of drugs in Phase III is to randomly allocate a new drug (ND) to patients in the sample, but with a greater fraction being administered ND if doing so is statistically justified. Such an adaptation is not possible in static trials designed with a sample size N in which approximately half the patients would receive the current drug or standard of care (SOC), despite evidence within the trial that ND is efficacious. We adapt a canonical stochastic multi-armed bandit algorithm named UCB1 to a clinical trials setting and analyse the resulting Type-2 error ß, as also minimum sample size N required by such a trial for a certain ß level. The difference in our proposal is not just in the allocation rule that applies to patients or volunteers in the trial, but also in the inference rule to decide if null hypothesis can be rejected. We also present simulations to establish that the ethical properties of such a trial are higher, both to verify our analysis and demonstate an empirical advantage when compared to 2 existing methods. In these simulations, we also propose and demonstrate a device to achieve low or comparable Type-1 error a vis-a-vis existing methods.

Suggested Citation

  • Mohammed Shahid Abdulla & L Ramprasath, 2021. "BBECT: Bandit -based Ethical Clinical Trials," Working papers 459, Indian Institute of Management Kozhikode.
    • Handle: RePEc:iik:wpaper:459
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    References listed on IDEAS

    as
    1. Gilles Stoltz & Sébastien Bubeck & Rémi Munos, 2011. "Pure exploration in finitely-armed and continuous-armed bandits," Post-Print hal-00609550, HAL.
    2. William F. Rosenberger & Nigel Stallard & Anastasia Ivanova & Cherice N. Harper & Michelle L. Ricks, 2001. "Optimal Adaptive Designs for Binary Response Trials," Biometrics, The International Biometric Society, vol. 57(3), pages 909-913, September.
    3. Biswas, Atanu & Bhattacharya, Rahul, 2011. "Optimal response-adaptive allocation designs in phase III clinical trials: Incorporating ethics in optimality," Statistics & Probability Letters, Elsevier, vol. 81(8), pages 1155-1160, August.
    4. 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.
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