Use of multiple imputation in the epidemiologic literature.
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.
Klebanoff
Mark A
MA
Division of Epidemiology, Statistics, and Prevention Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892-7510, USA. mk90h@nih.gov
Cole
Stephen R
SR
D000465:Algorithms; D000704:Analysis of Variance; D015982:Bias; D016001:Confidence Intervals; D015986:Confounding Factors, Epidemiologic; D003625:Data Collection / Q000379:methods; D003627:Data Interpretation, Statistical; D015991:Epidemiologic Measurements; D015340:Epidemiologic Research Design; D016021:Epidemiologic Studies; D006801:Humans; D053208:Kaplan-Meier Estimate; D011336:Probability; D016016:Proportional Hazards Models; D015203:Reproducibility of Results; D018401:Sample Size; D013269:Stochastic Processes