Empirical data are seldom complete. Missing data pose a threat to the validity of statistical inferences when missingness is not a completely random process. Model based multiple imputation (MI) can make uses of all available information in the data file to predict missing information and can produce valid statistical inferences in many scenarios. In this talk, I give an introduction to MI, discuss pros and cons of MI and demonstrate how to use the popular mice package in R to create model based multiple imputations of missing values. Finally, I also show how to specify more advanced imputation models (using further add-ons to the mice package) for example for longitudinal count data based on piecewise growth curve models assuming a zero-inflated Poisson or negative Binomial data generating process.