Multiple imputation using ICE: A simulation study on a binary response

Background: Various methods for multiple imputations of missing values are available in statistical software. They have been shown to work well when small proportions of missings were to be imputed. However, some researchers have started to impute large proportions of missings. Method: We performed a simulation using ICE on datasets of 50/100/200/400 cases and 4/11/25 variables. A varying proportion of data (3–63%) were randomly set missing and subsequently substituted by multiple imputation. Results: (1) It is shown when and how the algorithm breaks down by decreasing n of cases and increasing number of variables in the model. (2) Some unexpected results are demonstrated, e.g. flawed coefficients. (3) Compared to the second program that performs multiple imputations by chained equations, i.e., mice in R, the Stata program, ice, results in a slightly higher precision of the estimates by similar features of the program. Conclusion: The imputation of missings by chained equations is a useful tool for imputing small to moderate proportions of missings. The replacement of larger amounts, however, can be critical.