article Open AccessTop 1% cited
Accounting for missing data in statistical analyses: multiple imputation is not always the answer
International Journal of Epidemiology · 2019 · Vol. 48(4) · pp. 1294–1304
Rachael A. Hughes✉(University of Bristol)Jon Heron(MRC Epidemiology Unit)Jonathan A C Sterne(University of Bristol)Kate Tilling(NIHR Bristol Biomedical Research Centre)
Abstract
Choice of method for dealing with missing data is crucial for validity of conclusions, and should be based on careful consideration of the reasons for the missing data, missing data patterns and the availability of auxiliary information.
Statistical Methods and Bayesian InferenceAdvanced Causal Inference TechniquesStatistical Methods and InferenceMissing dataImputation (statistics)CovariateComputer scienceData miningStatisticsSelection biasEconometricsMathematics
MeSH terms
Data AccuracyData Interpretation, StatisticalHumansProbabilityResearch DesignBiasBiomedical Research
Funding
- NIHR Bristol Biomedical Research Centre
- National Institute for Health and Care Research
- British Heart Foundation
- Alcohol Research UK
- University of Bristol
- Medical Research Council
Citations
778
FWCI
79.88
field-weighted impact
References
72
Percentile
100%
vs. same field & year
Citations per year
References
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Biometrika · 1976 · 9,558 citations
Multiple imputation of missing blood pressure covariates in survival analysis
Statistics in Medicine · 1999 · 2,172 citations
Imputing missing covariate values for the Cox model
Statistics in Medicine · 2009 · 948 citations
Multiple imputation using chained equations: Issues and guidance for practice
Statistics in Medicine · 2010 · 9,312 citations
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