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Bounds on the average causal effects in randomized trials with noncompliance by covariate adjustment
Author(s) -
Shan Na,
Xu PingFeng
Publication year - 2016
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.201500157
Subject(s) - covariate , randomized controlled trial , monotonic function , randomized experiment , outcome (game theory) , statistics , mathematics , econometrics , causal inference , medicine , mathematical economics , mathematical analysis
In randomized trials with noncompliance, causal effects cannot be identified without strong assumptions. Therefore, several authors have considered bounds on the causal effects. Applying an idea of VanderWeele ([VanderWeele, T. J., 2008]), Chiba ([Chiba, Y., 2009]) gave bounds on the average causal effects in randomized trials with noncompliance using the information on the randomized assignment, the treatment received and the outcome under monotonicity assumptions about covariates. But he did not consider any observed covariates. If there are some observed covariates such as age, gender, and race in a trial, we propose new bounds using the observed covariate information under some monotonicity assumptions similar to those of VanderWeele and Chiba. And we compare the three bounds in a real example.