Matches in UGent Biblio for { <https://biblio.ugent.be/publication/5765135#aggregation> ?p ?o. }
Showing items 1 to 35 of
35
with 100 items per page.
- aggregation classification "A1".
- aggregation creator B917535.
- aggregation creator B917536.
- aggregation creator B917537.
- aggregation creator person.
- aggregation date "2014".
- aggregation format "application/pdf".
- aggregation hasFormat 5765135.bibtex.
- aggregation hasFormat 5765135.csv.
- aggregation hasFormat 5765135.dc.
- aggregation hasFormat 5765135.didl.
- aggregation hasFormat 5765135.doc.
- aggregation hasFormat 5765135.json.
- aggregation hasFormat 5765135.mets.
- aggregation hasFormat 5765135.mods.
- aggregation hasFormat 5765135.rdf.
- aggregation hasFormat 5765135.ris.
- aggregation hasFormat 5765135.txt.
- aggregation hasFormat 5765135.xls.
- aggregation hasFormat 5765135.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 "Science General".
- aggregation title "Improving upon the efficiency of complete case analysis when covariates are MNAR".
- aggregation abstract "Missing values in covariates of regression models are a pervasive problem in empirical research. Popular approaches for analyzing partially observed datasets include complete case analysis (CCA), multiple imputation (MI), and inverse probability weighting (IPW). In the case of missing covariate values, these methods (as typically implemented) are valid under different missingness assumptions. In particular, CCA is valid under missing not at random (MNAR) mechanisms in which missingness in a covariate depends on the value of that covariate, but is conditionally independent of outcome. In this paper, we argue that in some settings such an assumption is more plausible than the missing at random assumption underpinning most implementations of MI and IPW. When the former assumption holds, although CCA gives consistent estimates, it does not make use of all observed information. We therefore propose an augmented CCA approach which makes the same conditional independence assumption for missingness as CCA, but which improves efficiency through specification of an additional model for the probability of missingness, given the fully observed variables. The new method is evaluated using simulations and illustrated through application to data on reported alcohol consumption and blood pressure from the US National Health and Nutrition Examination Survey, in which data are likely MNAR independent of outcome.".
- aggregation authorList BK1297787.
- aggregation endPage "730".
- aggregation issue "4".
- aggregation startPage "719".
- aggregation volume "15".
- aggregation aggregates 5765168.
- aggregation isDescribedBy 5765135.
- aggregation similarTo kxu023.
- aggregation similarTo LU-5765135.