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Inference in response‐adaptive clinical trials when the enrolled population varies over time

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  • Massimiliano Russo
  • Steffen Ventz
  • Victoria Wang
  • Lorenzo Trippa

Abstract

A common assumption of data analysis in clinical trials is that the patient population, as well as treatment effects, do not vary during the course of the study. However, when trials enroll patients over several years, this hypothesis may be violated. Ignoring variations of the outcome distributions over time, under the control and experimental treatments, can lead to biased treatment effect estimates and poor control of false positive results. We propose and compare two procedures that account for possible variations of the outcome distributions over time, to correct treatment effect estimates, and to control type‐I error rates. The first procedure models trends of patient outcomes with splines. The second leverages conditional inference principles, which have been introduced to analyze randomized trials when patient prognostic profiles are unbalanced across arms. These two procedures are applicable in response‐adaptive clinical trials. We illustrate the consequences of trends in the outcome distributions in response‐adaptive designs and in platform trials, and investigate the proposed methods in the analysis of a glioblastoma study.

Suggested Citation

  • Massimiliano Russo & Steffen Ventz & Victoria Wang & Lorenzo Trippa, 2023. "Inference in response‐adaptive clinical trials when the enrolled population varies over time," Biometrics, The International Biometric Society, vol. 79(1), pages 381-393, March.
    • Handle: RePEc:bla:biomet:v:79:y:2023:i:1:p:381-393
    DOI: 10.1111/biom.13582
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    References listed on IDEAS

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    1. Fei Jiang & Lu Tian & Haoda Fu & Takahiro Hasegawa & L. J. Wei, 2019. "Robust Alternatives to ANCOVA for Estimating the Treatment Effect via a Randomized Comparative Study," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(528), pages 1854-1864, October.
    2. Guiteras, Raymond P. & Levine, David I. & Polley, Thomas H., 2016. "The pursuit of balance in sequential randomized trials," Development Engineering, Elsevier, vol. 1(C), pages 12-25.
    3. Simon N. Wood, 2003. "Thin plate regression splines," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 95-114, February.
    4. Steffen Ventz & William T. Barry & Giovanni Parmigiani & Lorenzo Trippa, 2017. "Bayesian response-adaptive designs for basket trials," Biometrics, The International Biometric Society, vol. 73(3), pages 905-915, September.
    5. Vaart,A. W. van der, 2000. "Asymptotic Statistics," Cambridge Books, Cambridge University Press, number 9780521784504, January.
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