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Novel criteria to exclude the surrogate paradox and their optimalities

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  • Yunjian Yin
  • Lan Liu
  • Zhi Geng
  • Peng Luo

Abstract

When the primary outcome is hard to collect, a surrogate endpoint is typically used as a substitute. However, even when a treatment has a positive average causal effect (ACE) on a surrogate endpoint, which also has a positive ACE on the primary outcome, it is still possible that the treatment has a negative ACE on the primary outcome. Such a phenomenon is called the surrogate paradox and greatly challenges the use of surrogates. In this paper, we provide criteria to exclude the surrogate paradox. Our criteria are optimal in the sense that they are sufficient and “almost necessary” to exclude the paradox: If the conditions are satisfied, the surrogate paradox is guaranteed to be absent, whereas if the conditions fail, there exists a data‐generating process with surrogate paradox that can generate the same observed data. That is, our criteria capture all the observed information to exclude the surrogate paradox.

Suggested Citation

  • Yunjian Yin & Lan Liu & Zhi Geng & Peng Luo, 2020. "Novel criteria to exclude the surrogate paradox and their optimalities," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(1), pages 84-103, March.
    • Handle: RePEc:bla:scjsta:v:47:y:2020:i:1:p:84-103
    DOI: 10.1111/sjos.12398
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