article Open AccessTop 10% cited
Comparison of imputation methods for missing laboratory data in medicine
BMJ Open · 2013 · Vol. 3(8) · pp. e002847–e002847
Akbar K. Waljee✉(VA Center for Clinical Management Research)Ashin Mukherjee(University of Michigan–Ann Arbor)Amit G. Singal(The University of Texas Southwestern Medical Center)Yiwei Zhang(University of Michigan–Ann Arbor)Jeffrey S. Warren(University of Michigan–Ann Arbor)Ulysses J. Balis(University of Michigan–Ann Arbor)Jorge A. Marrero(Southwestern Medical Center)Ji Zhu(University of Michigan–Ann Arbor)Peter Higgins(University of Michigan–Ann Arbor)
Abstract
MissForest is a highly accurate method of imputation for missing laboratory data and outperforms other common imputation techniques in terms of imputation error and maintenance of predictive ability with imputed values in two clinical predicative models.
Statistical Methods and Bayesian InferenceStatistical Methods in Clinical TrialsMeta-analysis and systematic reviewsImputation (statistics)Missing dataCategorical variableStatisticsCohortMedicineMultivariate statisticsData miningComputer scienceMathematics
Funding
- U.S. Department of Veterans Affairs
- University of Texas Southwestern Medical Center
- National Institutes of Health
- National Center for Advancing Translational Sciences
Citations
494
FWCI
7.82
field-weighted impact
References
12
Percentile
98%
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Citations per year
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References
A General Coefficient of Similarity and Some of Its Properties
Biometrics · 1971 · 5,151 citations
MissForest—non-parametric missing value imputation for mixed-type data
Bioinformatics · 2011 · 5,860 citations
Missing value estimation methods for DNA microarrays
Bioinformatics · 2001 · 4,180 citations
Multiple imputation of missing blood pressure covariates in survival analysis
Statistics in Medicine · 1999 · 2,172 citations
Random Forests
Machine Learning · 2001 · 121,242 citations
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