Multiple Imputation: A Review of Practical and Theoretical Findings

TL;DR

This paper reviews multiple imputation techniques for missing data, discussing theoretical foundations, practical strategies, recent advances, and future research directions in the field.

Contribution

It provides a comprehensive overview of both theoretical and practical aspects of multiple imputation, including recent methodological developments.

Findings

Comparison of different imputation methods on various criteria

Identification of promising future research directions

Discussion of theoretical results and their practical implications

Abstract

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.