Chapter 19 illustrates key features and technical details for multiple imputation-based (MI) analyses. Illustrations include analyses of the example data set, with focus on each of the three basic steps to MI: imputation, analysis, and inference. The flexibility from distinct steps for imputation and analysis is illustrated, and scenarios are detailed where MI is particularly useful, such as when covariates are missing, when outcome cannot be modeled using multivariate normal distributions such that likelihood-based analyses are difficult to implement, and when inclusive modeling strategies are used to help account for missing data.