Novel criteria to exclude the surrogate paradox and their optimalities

Yunjian Yin, Lan Liu, Zhi Geng, Peng Luo

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
JournalScandinavian Journal of Statistics
DOIs
StatePublished - Jan 1 2019

Bibliographical note

28pages, 1 figure

Keywords

  • average causal effect
  • optimality
  • surrogate paradox

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