The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
A Comparison of Multiple Imputation and Doubly Robust Estimation for Analyses with Missing Data
Abstract
Summary: Multiple imputation is now a well-established technique for analysing data sets where some units have incomplete observations. Provided that the imputation model is correct, the resulting estimates are consistent. An alternative, weighting by the inverse probability of observing complete data on a unit, is conceptually simple and involves fewer modelling assumptions, but it is known to be both inefficient (relative to a fully parametric approach) and sensitive to the choice of weighting model. Over the last decade, there has been a considerable body of theoretical work to improve the performance of inverse probability weighting, leading to the development of ‘doubly robust’ or ‘doubly protected’ estimators. We present an intuitive review of these developments and contrast these estimators with multiple imputation from both a theoretical and a practical viewpoint.
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Citations
Cites
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Rubin, D. B. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316696 · Cited by 20,701
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Cited by
Horton, N. J., & Kleinman, K. P. (2007) · The American Statistician · doi:10.1198/000313007x172556 · Cited by 786
Differential dropout and bias in randomised controlled trials: when it matters and when it may not
Bell, M. L., Kenward, M. G., Fairclough, D. L., & Horton, N. J. (2013) · BMJ · doi:10.1136/bmj.e8668 · Cited by 460
Self-citationMissing data: A statistical framework for practice
Carpenter, J. R., & Smuk, M. (2021) · Biometrical Journal · doi:10.1002/bimj.202000196 · Cited by 198
Self-citationAnalysis of tuberculosis prevalence surveys: new guidance on best-practice methods
Floyd, S., Sismanidis, C., Yamada, N., Daniel, R., Lagahid, J., Mecatti, F., Vianzon, R., Bloss, E., Tiemersma, E., Onozaki, I., et al. (2013) · Emerging Themes in Epidemiology · doi:10.1186/1742-7622-10-10 · Cited by 44
Metadata
- Authors
- Carpenter, J. R., Kenward, M. G., & Vansteelandt, S.
- Venue
- Journal of the Royal Statistical Society Series A: Statistics in Society
- Publisher
- Oxford University Press (OUP)
- Published
- 2006-01-24
- Volume
- 169
- Issue
- 3
- Pages
- 571-584
- ISSN
- 0964-1998 (ISSN-L)
- 1467-985X
- OpenAlex
- W2067332379
- Cited by
- 255
- References
- 26
- FWCI
- 9.15
- Type
- journal article
- Language
- en
- Open access
- closed
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- Carpenter, J. R., Kenward, M. G., & Vansteelandt, S. (2006). A Comparison of Multiple Imputation and Doubly Robust Estimation for Analyses with Missing Data. Journal of the Royal Statistical Society Series A: Statistics in Society. https://doi.org/10.1111/j.1467-985x.2006.00407.x
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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