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Measuring Surrogacy in Clinical Research

Author

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  • Rui Zhuang

    (University of Washington)

  • Ying Qing Chen

    (Fred Hutchinson Cancer Research Center)

Abstract

In clinical research, validated surrogate markers are highly desirable in study design, monitoring, and analysis, as they do not only reduce the required sample size and follow-up duration, but also facilitate scientific discoveries. However, challenges exist to identify a reliable marker. One particular statistical challenge arises on how to measure and rank the surrogacy of potential markers quantitatively. We review the main statistical methods for evaluating surrogate markers. In addition, we suggest a new measure, the so-called population surrogacy fraction of treatment effect, or simply the $$\rho $$ ρ -measure, in the setting of clinical trials. The $$\rho $$ ρ -measure carries an appealing population impact interpretation and supplements the existing statistical measures of surrogacy by providing “absolute” information. We apply the new measure along with other prominent measures to the HIV Prevention Trial Network 052 Study, a landmark trial for HIV/AIDS treatment-as-prevention.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:stabio:v:12:y:2020:i:3:d:10.1007_s12561-019-09244-4
    DOI: 10.1007/s12561-019-09244-4
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    References listed on IDEAS

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    1. Tomasz Burzykowski & Geert Molenberghs & Marc Buyse, 2004. "The validation of surrogate end points by using data from randomized clinical trials: a case‐study in advanced colorectal cancer," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(1), pages 103-124, February.
    2. Yun Li & Jeremy M.G. Taylor & Michael R. Elliott, 2010. "A Bayesian Approach to Surrogacy Assessment Using Principal Stratification in Clinical Trials," Biometrics, The International Biometric Society, vol. 66(2), pages 523-531, June.
    3. Tomasz Burzykowski & Geert Molenberghs & Marc Buyse & Helena Geys & Didier Renard, 2001. "Validation of surrogate end points in multiple randomized clinical trials with failure time end points," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(4), pages 405-422.
    4. 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.
    5. Constantine E. Frangakis & Donald B. Rubin, 2002. "Principal Stratification in Causal Inference," Biometrics, The International Biometric Society, vol. 58(1), pages 21-29, March.
    6. Hua Chen & Zhi Geng & Jinzhu Jia, 2007. "Criteria for surrogate end points," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(5), pages 919-932, November.
    7. Ariel Alonso & Geert Molenberghs & Tomasz Burzykowski & Didier Renard & Helena Geys & Ziv Shkedy & Fabián Tibaldi & José Cortiñas Abrahantes & Marc Buyse, 2004. "Prentice's Approach and the Meta-Analytic Paradigm: A Reflection on the Role of Statistics in the Evaluation of Surrogate Endpoints," Biometrics, The International Biometric Society, vol. 60(3), pages 724-728, September.
    8. Ying Huang & Peter B. Gilbert & Julian Wolfson, 2013. "Design and Estimation for Evaluating Principal Surrogate Markers in Vaccine Trials," Biometrics, The International Biometric Society, vol. 69(2), pages 301-309, June.
    9. Tyler J. VanderWeele, 2013. "Surrogate Measures and Consistent Surrogates," Biometrics, The International Biometric Society, vol. 69(3), pages 561-565, September.
    10. Dean Follmann, 2006. "Augmented Designs to Assess Immune Response in Vaccine Trials," Biometrics, The International Biometric Society, vol. 62(4), pages 1161-1169, December.
    11. Yongming Qu & Michael Case, 2007. "Quantifying the Effect of the Surrogate Marker by Information Gain," Biometrics, The International Biometric Society, vol. 63(3), pages 958-960, September.
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