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A simple solution to the inadequacy of asymptotic likelihood-based inference for response-adaptive clinical trials

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  • Alessandro Baldi Antognini

    (University of Bologna)

  • Marco Novelli

    (University of Bologna)

  • Maroussa Zagoraiou

    (University of Bologna)

Abstract

The present paper discusses drawbacks and limitations of likelihood-based inference in sequential clinical trials for treatment comparisons managed via Response-Adaptive Randomization. Taking into account the most common statistical models for the primary outcome—namely binary, Poisson, exponential and normal data—we derive the conditions under which (i) the classical confidence intervals degenerate and (ii) the Wald test becomes inconsistent and strongly affected by the nuisance parameters, also displaying a non monotonic power. To overcome these drawbacks, we provide a very simple solution that could preserve the fundamental properties of likelihood-based inference. Several illustrative examples and simulation studies are presented in order to confirm the relevance of our results and provide some practical recommendations.

Suggested Citation

  • Alessandro Baldi Antognini & Marco Novelli & Maroussa Zagoraiou, 2022. "A simple solution to the inadequacy of asymptotic likelihood-based inference for response-adaptive clinical trials," Statistical Papers, Springer, vol. 63(1), pages 157-180, February.
    • Handle: RePEc:spr:stpapr:v:63:y:2022:i:1:d:10.1007_s00362-021-01234-3
    DOI: 10.1007/s00362-021-01234-3
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    References listed on IDEAS

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    5. D. Azriel & M. Mandel & Y. Rinott, 2012. "Optimal allocation to maximize the power of two-sample tests for binary response," Biometrika, Biometrika Trust, vol. 99(1), pages 101-113.
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