The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Multiple imputation: current perspectives
Abstract
This paper provides an overview of multiple imputation and current perspectives on its use in medical research. We begin with a brief review of the problem of handling missing data in general and place multiple imputation in this context, emphasizing its relevance for longitudinal clinical trials and observational studies with missing covariates. We outline how multiple imputation proceeds in practice and then sketch its rationale. We explore the problem of obtaining proper imputations in some detail and distinguish two main classes of approach, methods based on fully multivariate models, and those that iterate conditional univariate models. We show how the use of so-called uncongenial imputation models are particularly valuable for sensitivity analyses and also for certain analyses in clinical trial settings. We also touch upon other forms of sensitivity analysis that use multiple imputation. Finally, we give some open questions that the increasing use of multiple imputation has thrown up, which we believe are useful directions for future research.
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Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Multiple Imputation for Nonresponse in Surveys
Rubin, D. B. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316696 · Cited by 20,701
RUBIN, D. B. (1976) · Biometrika · doi:10.1093/biomet/63.3.581 · Cited by 9,925
Statistical Analysis with Missing Data
Little, R. J. A., & Rubin, D. B. (2002) · Wiley Series in Probability and Statistics · doi:10.1002/9781119013563 · Cited by 8,473
Analysis of Incomplete Multivariate Data
Schafer, J. L. (1997) · doi:10.1201/9781439821862 · Cited by 6,012
Multiple imputation after 18+ years
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Schafer, J. L. (1999) · Statistical Methods in Medical Research · doi:10.1177/096228029900800102 · Cited by 3,949
Multiple Imputation of Missing Values
Royston, P. (2004) · The Stata Journal: Promoting communications on statistics and Stata · doi:10.1177/1536867x0400400301 · Cited by 2,340
Cited by
Multiple imputation using chained equations: Issues and guidance for practice
White, I. R., Royston, P., & Wood, A. M. (2011) · Statistics in Medicine · doi:10.1002/sim.4067 · Cited by 9,792
Boateng, G. O., Neilands, T. B., Frongillo, E. A., Melgar-Quiñonez, H. R., & Young, S. L. (2018) · Frontiers in Public Health · doi:10.3389/fpubh.2018.00149 · Cited by 5,925
Principled missing data methods for researchers
Dong, Y., & Peng, C.-Y. J. (2013) · SpringerPlus · doi:10.1186/2193-1801-2-222 · Cited by 2,208
Multiple Imputation by Chained Equations (MICE): Implementation in Stata
Royston, P., & White, I. (2011) · Journal of Statistical Software · doi:10.18637/jss.v045.i04 · Cited by 1,248
Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK): Explanation and Elaboration
Altman, D. G., McShane, L. M., Sauerbrei, W., & Taube, S. E. (2012) · PLoS Medicine · doi:10.1371/journal.pmed.1001216 · Cited by 1,013
Metadata
- Authors
- Kenward, M. G., & Carpenter, J.
- Venue
- Statistical Methods in Medical Research
- Publisher
- SAGE Publications
- Published
- 2007-06
- Volume
- 16
- Issue
- 3
- Pages
- 199-218
- ISSN
- 0962-2802 (ISSN-L)
- 1477-0334
- OpenAlex
- W2011054301
- Cited by
- 456
- References
- 8
- FWCI
- 18.02
- Type
- journal article
- Language
- en
- Open access
- closed
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
