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Novel criteria to exclude the surrogate paradox and their optimalities
Author(s) -
Yin Yunjian,
Liu Lan,
Geng Zhi,
Luo Peng
Publication year - 2020
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/sjos.12398
Subject(s) - surrogate endpoint , surrogate data , surrogate model , outcome (game theory) , mathematics , clinical endpoint , econometrics , statistics , mathematical economics , medicine , clinical trial , physics , quantum mechanics , nonlinear system
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.