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American Journal of Epidemiology Advance Access originally published online on June 30, 2008
American Journal of Epidemiology 2008 168(4):355-357; doi:10.1093/aje/kwn071
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American Journal of Epidemiology Published by the Johns Hopkins Bloomberg School of Public Health 2008.

Use of Multiple Imputation in the Epidemiologic Literature

Mark A. Klebanoff1 and Stephen R. Cole2

1 Division of Epidemiology, Statistics, and Prevention Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Department of Health and Human Services, Bethesda, MD
2 Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD

Correspondence to Dr. Mark A. Klebanoff, Division of Epidemiology, Statistics, and Prevention Research, National Institute of Child Health and Human Development, National Institutes of Health, Department of Health and Human Services, 6100 Executive Boulevard, Room 7B05F, MSC 7510, Bethesda, MD 20892-7510 (e-mail: mk90h{at}nih.gov).

Received for publication January 14, 2008. Accepted for publication March 4, 2008.

The authors attempted to catalog the use of procedures to impute missing data in the epidemiologic literature and to determine the degree to which imputed results differed in practice from unimputed results. The full text of articles published in 2005 and 2006 in four leading epidemiologic journals was searched for the text imput. Sixteen articles utilizing multiple imputation, inverse probability weighting, or the expectation-maximization algorithm to impute missing data were found. The small number of relevant manuscripts and diversity of detail provided precluded systematic analysis of the use of imputation procedures. To form a bridge between current and future practice, the authors suggest details that should be included in articles that utilize these procedures.

expectation; imputation; missing data; probability weighting


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