Multiple imputation using chained equations: Issues and guidance for practice

TLDR

Multiple imputation by chained equations is a flexible, practical method for handling missing data. The paper aims to describe the principles of multiple imputation by chained equations and provide guidance on specifying the imputation model and determining the number of imputations. The authors illustrate the method with a mental health dataset, provide Stata code fragments, and describe practical analysis steps including model building and checking. They highlight limitations and potential pitfalls of multiple imputation by chained equations. © 2010 John Wiley & Sons, Ltd.

Abstract

Abstract Multiple imputation by chained equations is a flexible and practical approach to handling missing data. We describe the principles of the method and show how to impute categorical and quantitative variables, including skewed variables. We give guidance on how to specify the imputation model and how many imputations are needed. We describe the practical analysis of multiply imputed data, including model building and model checking. We stress the limitations of the method and discuss the possible pitfalls. We illustrate the ideas using a data set in mental health, giving Stata code fragments. Copyright © 2010 John Wiley & Sons, Ltd.