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The Efficient Covariate-Adaptive Design for high-order balancing of quantitative and qualitative covariates

Author

Listed:
  • Alessandro Baldi Antognini

    (University of Bologna)

  • Rosamarie Frieri

    (University of Bologna)

  • Maroussa Zagoraiou

    (University of Bologna)

  • Marco Novelli

    (University of Bologna)

Abstract

In the context of sequential treatment comparisons, the acquisition of covariate information about the statistical units is crucial for the validity of the trial. Furthermore, balancing the assignments among covariates is of primary importance, since the potential imbalance of the covariate distributions across the groups can severely undermine the statistical analysis. For this reason, several covariate-adaptive randomization procedures have been suggested in the literature, but most of them only apply to categorical factors. In this paper we propose a new class of rules, called the Efficient Covariate-Adaptive Design, which is high-order balanced regardless of the number of factors and their nature (qualitative and/or quantitative), also accounting for every order covariate effects and interactions. The suggested procedure performs very well, is flexible and simple to implement. The advantages of our proposal are also analyzed via simulations and its finite sample properties are compared with those of other well-known rules, by also including the redesign of a real clinical trial.

Suggested Citation

  • Alessandro Baldi Antognini & Rosamarie Frieri & Maroussa Zagoraiou & Marco Novelli, 2024. "The Efficient Covariate-Adaptive Design for high-order balancing of quantitative and qualitative covariates," Statistical Papers, Springer, vol. 65(1), pages 19-44, February.
    • Handle: RePEc:spr:stpapr:v:65:y:2024:i:1:d:10.1007_s00362-022-01381-1
    DOI: 10.1007/s00362-022-01381-1
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    References listed on IDEAS

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    1. Jun Shao & Xinxin Yu & Bob Zhong, 2010. "A theory for testing hypotheses under covariate-adaptive randomization," Biometrika, Biometrika Trust, vol. 97(2), pages 347-360.
    2. Wei Ma & Feifang Hu & Lixin Zhang, 2015. "Testing Hypotheses of Covariate-Adaptive Randomized Clinical Trials," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(510), pages 669-680, June.
    3. Anthony C. Atkinson, 2002. "The comparison of designs for sequential clinical trials with covariate information," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(2), pages 349-373, June.
    4. Quan Zhou & Philip A Ernst & Kari Lock Morgan & Donald B Rubin & Anru Zhang, 2018. "Sequential rerandomization," Biometrika, Biometrika Trust, vol. 105(3), pages 745-752.
    5. A. Baldi Antognini & M. Zagoraiou, 2011. "The covariate-adaptive biased coin design for balancing clinical trials in the presence of prognostic factors," Biometrika, Biometrika Trust, vol. 98(3), pages 519-535.
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