A practical guide to multiple imputation of missing data in nephrology

Health data are often plagued with missing values that can greatly reduce the sample size if only complete cases are considered for analysis. Furthermore, analyses that ignore missing data have the potential to introduce bias in the parameter estimates. Multiple imputation techniques have been developed to recover the information that would otherwise be lost when excluding observations with missing data and to help minimize bias. However, the validity of analyses using imputed data relies on the imputation model having been correctly specified. The aim of this guide is to aid the reader in the decision-making process when conducting an analysis with multiply imputed data in the context of nephrology research. We discuss (i) missing mechanism assumption, (ii) imputation method, (iii) imputation model, (iv) derived variables, (v) the number of imputed data sets, (vi) diagnostic checks, (vii) analysis and pooling of results, and (viii) reporting the results. This process is demonstrated using data from the National Health and Nutrition Examination Survey to explore the association between hypertension and kidney disease in adults from the general population. Example code is provided for SAS software and the mice package in R.

Automated summary

Health data often have missing values, and utilizing multiple imputation techniques can help reduce bias and maintain sample size. Correct specification of the imputation model is crucial for the validity of analyses. Considerations such as missing mechanism, imputation method, and result reporting are important when conducting research with multiply imputed data.