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Multiple imputation in public health research
Author(s) -
Zhou XiaoHua,
Eckert George J.,
Tierney William M.
Publication year - 2001
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.689
Subject(s) - imputation (statistics) , covariate , missing data , computer science , statistics , standard deviation , data mining , mathematics
Missing data in public health research is a major problem. Mean or median imputation is frequently used because it is easy to implement. Although multiple imputation has good statistical properties, it is not yet used extensively. For two real studies and a real study‐based simulation, we compared the results after using multiple imputation against several simpler imputation methods. All imputation methods showed similar results for both real studies, but somewhat different results were obtained when only complete cases were used. The simulation showed large differences among various multiple imputation methods with a different number of variables for creating the matching metric for multiple imputation. Multiple imputation using only a few covariates in the matching model produced more biased coefficient estimates than using all available covariates in the matching model. The simulation also showed better standard deviation estimates for multiple imputation than for single mean imputation. Copyright © 2001 John Wiley & Sons, Ltd.