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A Surrogate Measure for Time-Varying Biomarkers in Randomized Clinical Trials

Author

Listed:
  • Rui Zhuang

    (Department of Biostatistics, University of Washington, Seattle, WA 98195, USA)

  • Fan Xia

    (Department of Biostatistics, University of Washington, Seattle, WA 98195, USA)

  • Yixin Wang

    (Department of Medicine, Stanford University, Palo Alto, CA 94305, USA)

  • Ying-Qing Chen

    (Department of Medicine, Stanford University, Palo Alto, CA 94305, USA)

Abstract

Clinical trials with rare or distant outcomes are usually designed to be large in size and long term. The resource-demand and time-consuming characteristics limit the feasibility and efficiency of the studies. There are motivations to replace rare or distal clinical endpoints by reliable surrogate markers, which could be earlier and easier to collect. However, statistical challenges still exist to evaluate and rank potential surrogate markers. In this paper, we define a generalized proportion of treatment effect for survival settings. The measure’s definition and estimation do not rely on any model assumption. It is equipped with a consistent and asymptotically normal non-parametric estimator. Under proper conditions, the measure reflects the proportion of average treatment effect mediated by the surrogate marker among the group that would survive to mark the measurement time under both intervention and control arms.

Suggested Citation

  • Rui Zhuang & Fan Xia & Yixin Wang & Ying-Qing Chen, 2022. "A Surrogate Measure for Time-Varying Biomarkers in Randomized Clinical Trials," Mathematics, MDPI, vol. 10(4), pages 1-17, February.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:4:p:584-:d:748538
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    References listed on IDEAS

    as
    1. Rui Zhuang & Ying Qing Chen, 2020. "Measuring Surrogacy in Clinical Research," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 12(3), pages 295-323, December.
    2. Yue Wang & Jeremy M. G. Taylor, 2002. "A Measure of the Proportion of Treatment Effect Explained by a Surrogate Marker," Biometrics, The International Biometric Society, vol. 58(4), pages 803-812, December.
    3. Jeremy M. G. Taylor & Yue Wang & Rodolphe Thiébaut, 2005. "Counterfactual Links to the Proportion of Treatment Effect Explained by a Surrogate Marker," Biometrics, The International Biometric Society, vol. 61(4), pages 1102-1111, December.
    Full references (including those not matched with items on IDEAS)

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