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New insights into adaptive enrichment designs

Author

Listed:
  • Alessandro Baldi Antognini

    (University of Bologna)

  • Rosamarie Frieri

    (University of Bologna)

  • Maroussa Zagoraiou

    (University of Bologna)

Abstract

The transition towards personalized medicine is happening and the new experimental framework is raising several challenges, from a clinical, ethical, logistical, regulatory, and statistical perspective. To face these challenges, innovative study designs with increasing complexity have been proposed. In particular, adaptive enrichment designs are becoming more attractive for their flexibility. However, these procedures rely on an increasing number of parameters that are unknown at the planning stage of the clinical trial, so the study design requires particular care. This review is dedicated to adaptive enrichment studies with a focus on design aspects. While many papers deal with methods for the analysis, the sample size determination and the optimal allocation problem have been overlooked. We discuss the multiple aspects involved in adaptive enrichment designs that contribute to their advantages and disadvantages. The decision-making process of whether or not it is worth enriching should be driven by clinical and ethical considerations as well as scientific and statistical concerns.

Suggested Citation

  • Alessandro Baldi Antognini & Rosamarie Frieri & Maroussa Zagoraiou, 2023. "New insights into adaptive enrichment designs," Statistical Papers, Springer, vol. 64(4), pages 1305-1328, August.
    • Handle: RePEc:spr:stpapr:v:64:y:2023:i:4:d:10.1007_s00362-023-01433-0
    DOI: 10.1007/s00362-023-01433-0
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    References listed on IDEAS

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    1. Nigel Stallard, 2023. "Rejoinder to discussion on “Adaptive enrichment designs with a continuous biomarker”," Biometrics, The International Biometric Society, vol. 79(1), pages 36-38, March.
    2. M. Rosenblum & M. J. van der Laan, 2011. "Optimizing randomized trial designs to distinguish which subpopulations benefit from treatment," Biometrika, Biometrika Trust, vol. 98(4), pages 845-860.
    3. Michael Rosenblum & Ethan X. Fang & Han Liu, 2020. "Optimal, two‐stage, adaptive enrichment designs for randomized trials, using sparse linear programming," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(3), pages 749-772, July.
    4. Nigel Stallard, 2023. "Adaptive enrichment designs with a continuous biomarker," Biometrics, The International Biometric Society, vol. 79(1), pages 9-19, March.
    5. Sergey Tarima & Nancy Flournoy, 2022. "Most powerful test sequences with early stopping options," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 85(4), pages 491-513, May.
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