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On the use of surrogate end points in randomized trials

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  • C. B. Begg
  • D. H. Y. Leung

Abstract

In a recent paper Day and Duffy proposed a strategy for designing a randomized trial of different breast cancer screening schedules. Their strategy was based on the use of predictors of mortality determined by patients' factors at diagnosis as surrogates for true mortality. On the basis of the Prentice criterion for validity of a surrogate end point, and data from earlier studies of breast cancer case survival, they showed that, not only would the trial require a much shorter follow‐up, but also that the information (i.e. inverse variance) for evaluating a treatment effect on mortality would be greater by a factor of nearly 3 if the predictors of mortality were used, compared with a trial in which mortality was actually observed. Although these results are technically correct, we believe that the conceptual strategy on which they are based is flawed, and that the fundamental problem is the Prentice criterion itself. In this paper the technical issues are discussed in detail, and an alternative structure for evaluating the validity of surrogate end points is proposed.

Suggested Citation

  • C. B. Begg & D. H. Y. Leung, 2000. "On the use of surrogate end points in randomized trials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 163(1), pages 15-28.
    • Handle: RePEc:bla:jorssa:v:163:y:2000:i:1:p:15-28
    DOI: 10.1111/1467-985X.00153
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    Cited by:

    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. Jiafeng Chen & David M. Ritzwoller, 2021. "Semiparametric Estimation of Long-Term Treatment Effects," Papers 2107.14405, arXiv.org, revised Aug 2023.
    3. Susan Athey & Raj Chetty & Guido Imbens & Hyunseung Kang, 2016. "Estimating Treatment Effects using Multiple Surrogates: The Role of the Surrogate Score and the Surrogate Index," Papers 1603.09326, arXiv.org, revised Aug 2024.
    4. Keith Battocchi & Eleanor Dillon & Maggie Hei & Greg Lewis & Miruna Oprescu & Vasilis Syrgkanis, 2021. "Estimating the Long-Term Effects of Novel Treatments," Papers 2103.08390, arXiv.org, revised Feb 2022.
    5. Baojiang Chen & Jing Qin, 2014. "Test the reliability of doubly robust estimation with missing response data," Biometrics, The International Biometric Society, vol. 70(2), pages 289-298, June.
    6. Song Xi Chen & Denis H. Y. Leung & Jing Qin, 2008. "Improving semiparametric estimation by using surrogate data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(4), pages 803-823, September.
    7. Stuart G. Baker & Grant Izmirlian & Victor Kipnis, 2005. "Resolving paradoxes involving surrogate end points," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(4), pages 753-762, November.
    8. Chen, Jiafeng & Ritzwoller, David M., 2023. "Semiparametric estimation of long-term treatment effects," Journal of Econometrics, Elsevier, vol. 237(2).
    9. Christine Mulhern & Isaac M. Opper, 2021. "Measuring and Summarizing the Multiple Dimensions of Teacher Effectiveness," CESifo Working Paper Series 9263, CESifo.
    10. Denni Tommasi & Arthur Lewbel & Rossella Calvi, 2017. "LATE with Mismeasured or Misspecified Treatment: An application to Women's Empowerment in India," Working Papers ECARES ECARES 2017-27, ULB -- Universite Libre de Bruxelles.
    11. Rahul Singh, 2022. "Generalized Kernel Ridge Regression for Long Term Causal Inference: Treatment Effects, Dose Responses, and Counterfactual Distributions," Papers 2201.05139, arXiv.org.
    12. Isaac Meza & Rahul Singh, 2021. "Nested Nonparametric Instrumental Variable Regression: Long Term, Mediated, and Time Varying Treatment Effects," Papers 2112.14249, arXiv.org, revised Mar 2024.
    13. Banerjee, Buddhananda & Biswas, Atanu, 2015. "Linear increment in efficiency with the inclusion of surrogate endpoint," Statistics & Probability Letters, Elsevier, vol. 96(C), pages 102-108.

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