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- aggregation classification "A1".
- aggregation creator B571930.
- aggregation creator person.
- aggregation date "2011".
- aggregation format "application/pdf".
- aggregation hasFormat 1234518.bibtex.
- aggregation hasFormat 1234518.csv.
- aggregation hasFormat 1234518.dc.
- aggregation hasFormat 1234518.didl.
- aggregation hasFormat 1234518.doc.
- aggregation hasFormat 1234518.json.
- aggregation hasFormat 1234518.mets.
- aggregation hasFormat 1234518.mods.
- aggregation hasFormat 1234518.rdf.
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- aggregation hasFormat 1234518.txt.
- aggregation hasFormat 1234518.xls.
- aggregation hasFormat 1234518.yaml.
- aggregation isPartOf urn:issn:1465-4644.
- aggregation language "eng".
- aggregation rights "I have transferred the copyright for this publication to the publisher".
- aggregation subject "Mathematics and Statistics".
- aggregation title "Doubly robust estimation of attributable fractions".
- aggregation abstract "The attributable fraction (AF) is a widely used measure to assess the impact of an exposure on a disease. It is commonly estimated through maximum likelihood, which requires a regression model for the outcome. Recently, it was demonstrated that the AF can also be estimated through inverse probability weighting, which requires a model for the exposure. In this paper, we derive doubly robust estimators for the AF. These estimators require one model for the outcome and one model for the exposure and are consistent if either model is correct, not necessarily both. We consider both cohort/cross-sectional studies and case-control studies.".
- aggregation authorList BK922756.
- aggregation endPage "121".
- aggregation issue "1".
- aggregation startPage "112".
- aggregation volume "12".
- aggregation aggregates 1234606.
- aggregation isDescribedBy 1234518.
- aggregation similarTo kxq049.
- aggregation similarTo LU-1234518.