Multiple imputations for missing data: a simulation with epidemiological data

In situations with missing data, statistical analyses are usually limited to subjects with complete data. However, such estimates may be biased. The method of "filling in" missing data is called imputation. This article aimed to present a multiple imputation method. From a data set of 470 surgical patients, logistic models were developed for death as the outcome. Two incomplete data sets were generated: one with 5% and another with 20% of missing data in a single variable. Logistic models were fitted for the complete and incomplete data sets and for the data set completed by multiple imputations. Estimates obtained for the data set with missing data were different from those observed in the complete data set, mainly in the situation with 20% of missing data. The multiple imputation used here appeared efficient, producing very similar results to those obtained with the complete data set. However, one coefficient became non-significant. The analysis using multiple imputations was considered superior to using the data sets that excluded incomplete cases from the analysis.