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Sharp bounds on causal effects using a surrogate endpoint
Author(s) -
Kuroki Manabu
Publication year - 2013
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.5873
Subject(s) - confounding , monotonic function , econometrics , causality (physics) , causal inference , surrogate endpoint , mathematics , clinical endpoint , point (geometry) , statistics , randomized controlled trial , medicine , physics , mathematical analysis , geometry , quantum mechanics
This paper considers a problem of evaluating the causal effect of a treatment X on a true endpoint Y using a surrogate endpoint S , in the presence of unmeasured confounders between S and Y . Such confounders render the causal effect of X on Y unidentifiable from the causal effect of X on S and the joint probability of S and Y . To evaluate the causal effect of X on Y in such a situation, this paper derives closed‐form formulas for the sharp bounds on the causal effect of X on Y based on both the causal effect of X on S and the joint probability of S and Y under various assumptions. In addition, we show that it is not always necessary to observe Y to test the null causal effect of X on Y under the monotonicity assumption between X and S . These bounds enable clinical practitioners and researchers to assess the causal effect of a treatment on a true endpoint using a surrogate endpoint with minimum computational effort. Copyright © 2013 John Wiley & Sons, Ltd.