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Criteria for surrogate end points

Author

Listed:
  • Hua Chen
  • Zhi Geng
  • Jinzhu Jia

Abstract

Summary. A surrogate end point is often used to evaluate the effects of treatments or exposures on the true end point in medical researches. Various criteria for the statistical surrogate, principal surrogate and strong surrogate have been proposed. We first illustrate that, with a surrogate end point that is defined by these criteria, it is possible that a treatment has a positive effect on the surrogate, which in turn has a positive effect on the true end point, but the treatment has a negative effect on the true end point. We define such a phenomenon as a surrogate paradox. The surrogate paradox also means that the sign of the treatment effect on the true end point is unpredictable by the effect signs of both the treatment on the surrogate and the surrogate on the true end point. Then we propose two notions for a consistent surrogate and a strictly consistent surrogate to avoid the surrogate paradox. With the causal network that was presented by Lauritzen, we discuss the conditions for a strong surrogate to be a consistent surrogate and a strictly consistent surrogate.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:jorssb:v:69:y:2007:i:5:p:919-932
    DOI: 10.1111/j.1467-9868.2007.00617.x
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    References listed on IDEAS

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    1. D. R. Cox & Nanny Wermuth, 2003. "A general condition for avoiding effect reversal after marginalization," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(4), pages 937-941, November.
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    3. Steffen L. Lauritzen, 2004. "Discussion on Causality," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 31(2), pages 189-193, June.
    4. Constantine E. Frangakis & Donald B. Rubin, 2002. "Principal Stratification in Causal Inference," Biometrics, The International Biometric Society, vol. 58(1), pages 21-29, March.
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    Citations

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    Cited by:

    1. Gilbert Peter B. & Gabriel Erin E. & Huang Ying & Chan Ivan S.F., 2015. "Surrogate Endpoint Evaluation: Principal Stratification Criteria and the Prentice Definition," Journal of Causal Inference, De Gruyter, vol. 3(2), pages 157-175, September.
    2. 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.
    3. Ying Huang & Shibasish Dasgupta, 2019. "Likelihood-Based Methods for Assessing Principal Surrogate Endpoints in Vaccine Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(3), pages 504-523, December.
    4. 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.
    5. Guido Imbens & Nathan Kallus & Xiaojie Mao & Yuhao Wang, 2022. "Long-term Causal Inference Under Persistent Confounding via Data Combination," Papers 2202.07234, arXiv.org, revised Aug 2024.
    6. Fatema Shafie Khorassani & Jeremy M. G. Taylor & Niko Kaciroti & Michael R. Elliott, 2023. "Incorporating Covariates into Measures of Surrogate Paradox Risk," Stats, MDPI, vol. 6(1), pages 1-23, February.
    7. Tyler J. VanderWeele, 2013. "Surrogate Measures and Consistent Surrogates," Biometrics, The International Biometric Society, vol. 69(3), pages 561-565, September.
    8. Zhichao Jiang & Peng Ding & Zhi Geng, 2016. "Principal causal effect identification and surrogate end point evaluation by multiple trials," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(4), pages 829-848, September.
    9. Corwin M. Zigler & Thomas R. Belin, 2012. "A Bayesian Approach to Improved Estimation of Causal Effect Predictiveness for a Principal Surrogate Endpoint," Biometrics, The International Biometric Society, vol. 68(3), pages 922-932, September.
    10. Bingbo Gao & Jianyu Yang & Ziyue Chen & George Sugihara & Manchun Li & Alfred Stein & Mei-Po Kwan & Jinfeng Wang, 2023. "Causal inference from cross-sectional earth system data with geographical convergent cross mapping," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    11. Ting-Chih Hung & Yu-Chang Chen, 2026. "The Proximal Surrogate Index: Long-Term Treatment Effects under Unobserved Confounding," Papers 2601.17712, arXiv.org.
    12. Chuan Ju & Zhi Geng, 2010. "Criteria for surrogate end points based on causal distributions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(1), pages 129-142, January.
    13. Mealli Fabrizia & Mattei Alessandra, 2012. "A Refreshing Account of Principal Stratification," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-19, April.
    14. VanderWeele Tyler J, 2011. "Principal Stratification -- Uses and Limitations," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-14, July.
    15. Marshall M. Joffe, 2013. "Discussion on “Surrogate Measures and Consistent Surrogates”," Biometrics, The International Biometric Society, vol. 69(3), pages 569-573, September.
    16. Jeremy Yang & Dean Eckles & Paramveer Dhillon & Sinan Aral, 2024. "Targeting for Long-Term Outcomes," Management Science, INFORMS, vol. 70(6), pages 3841-3855, June.
    17. 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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