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- aggregation classification "A1".
- aggregation creator B190423.
- aggregation creator person.
- aggregation creator person.
- aggregation date "2008".
- aggregation hasFormat 594185.bibtex.
- aggregation hasFormat 594185.csv.
- aggregation hasFormat 594185.dc.
- aggregation hasFormat 594185.didl.
- aggregation hasFormat 594185.doc.
- aggregation hasFormat 594185.json.
- aggregation hasFormat 594185.mets.
- aggregation hasFormat 594185.mods.
- aggregation hasFormat 594185.rdf.
- aggregation hasFormat 594185.ris.
- aggregation hasFormat 594185.txt.
- aggregation hasFormat 594185.xls.
- aggregation hasFormat 594185.yaml.
- aggregation isPartOf urn:issn:1369-7412.
- aggregation language "eng".
- aggregation publisher "WILEY-BLACKWELL PUBLISHING, INC".
- aggregation rights "I don't know the status of the copyright for this publication".
- aggregation subject "Mathematics and Statistics".
- aggregation title "Estimation of controlled direct effects".
- aggregation abstract "When regression models adjust for mediators on the causal path from exposure to outcome, the regression coefficient of exposure is commonly viewed as a measure of the direct exposure effect. This interpretation can be misleading, even with a randomly assigned exposure. This is because adjustment for post-exposure measurements introduces bias whenever their association with the outcome is confounded by more than just the exposure. By the same token, adjustment for such confounders stays problematic when these are themselves affected by the exposure. Robins accommodated this by introducing linear structural nested direct effect models with direct effect parameters that can be estimated by using inverse probability weighting by a conditional distribution of the mediator. The resulting estimators are consistent, but inefficient, and can be extremely unstable when the mediator is absolutely continuous. We develop direct effect estimators which are not only more efficient but also consistent under a less demanding model for a conditional expectation of the outcome. We find that the one estimator which avoids inverse probability weighting altogether performs best. This estimator is intuitive, computationally straightforward and, as demonstrated by simulation, competes extremely well with ordinary least squares estimators in settings where standard regression is valid.".
- aggregation authorList BK448996.
- aggregation endPage "4066".
- aggregation issue "5".
- aggregation startPage "1049".
- aggregation volume "70".
- aggregation isDescribedBy 594185.
- aggregation similarTo LU-594185.