Multiple imputation is a straightforward method for handling missing data in a principled fashion. This paper presents an overview of multiple imputation, including important theoretical results and their practical implications for generating and using multiple imputations. A review of strategies for generating imputations follows, including recent developments in flexible joint modeling and sequential regression/chained equations/fully conditional specification approaches. Finally, we compare and contrast different methods for generating imputations on a range of criteria before identifying promising avenues for future research.
"Multiple Imputation: A Review of Practical and Theoretical Findings." Statist. Sci. 33 (2) 142 - 159, May 2018. https://doi.org/10.1214/18-STS644