article Open AccessTop 1% cited
Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls
BMJ · 2009 · Vol. 338(jun29 1) · pp. b2393–b2393
Jonathan A C Sterne✉(University of Bristol)Ian R. White(MRC Biostatistics Unit)John B. Carlin(Murdoch Children's Research Institute)Michael Spratt(University of Bristol)Patrick Royston(MRC Clinical Trials Unit at UCL)Michael G. Kenward(University of London)Angela WoodJames R. Carpenter(London School of Hygiene & Tropical Medicine)
Abstract
Most studies have some missing data. Jonathan Sterne and colleagues describe the appropriate use and reporting of the multiple imputation approach to dealing with them
Statistical Methods and Bayesian InferenceStatistical Methods and InferenceSurvey Methodology and NonresponseImputation (statistics)Missing dataComputer scienceData scienceData miningStatisticsEconometricsMathematicsMachine learning
MeSH terms
Data CollectionRandom AllocationResearch DesignBiasBiomedical Research
Funding
- Economic and Social Research Council
Citations
6,987
FWCI
111.69
field-weighted impact
References
30
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100%
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References
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Multiple imputation of missing blood pressure covariates in survival analysis
Statistics in Medicine · 1999 · 2,172 citations
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PLoS Medicine · 2007 · 21,248 citations
Strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies
BMJ · 2007 · 9,537 citations
Statistical Analysis with Missing Data
Technometrics · 1988 · 3,119 citations
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