Multiple imputation in public health research.
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.
Zhou
X H
XH
Division of Biostatistics, Department of Medicine, Indiana University School of Medicine, Indianapolis 46202, USA. azhou@iupui.edu
Eckert
G J
GJ
Tierney
W M
WM
D001249:Asthma / Q000401:mortality; D003198:Computer Simulation; D005260:Female; D006801:Humans; D016015:Logistic Models; D008173:Lung Diseases, Obstructive / Q000401:mortality; D008297:Male; D008954:Models, Biological; D009010:Monte Carlo Method; D017060:Patient Satisfaction; D010817:Physician-Patient Relations; D011634:Public Health / Q000379:methods*; D013223:Statistics as Topic / Q000379:methods*