Multiple imputation of public health research

Xiao Hua Zhou, George J. Eckert, William M. Tierney

Research output: Contribution to journalArticle

80 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1541-1549
Number of pages9
JournalStatistics in Medicine
Volume20
Issue number9-10
DOIs
StatePublished - May 15 2001

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ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

Cite this

Zhou, X. H., Eckert, G. J., & Tierney, W. M. (2001). Multiple imputation of public health research. Statistics in Medicine, 20(9-10), 1541-1549. https://doi.org/10.1002/sim.689