Empirical Comparison of Imputation Methods for Multivariate Missing Data in Public Health

Sample estimates derived from data with missing values may be unreliable and may negatively impact the inferences that researchers make about the underlying population due to nonresponse bias. As a result, imputation is often preferred to listwise deletion in handling multivariate missing data. In this study, we compared three popular imputation methods: sequential multiple imputation, fractional hot-deck imputation, and generalized efficient regression-based imputation with latent processes for handling multivariate missingness under different missing patterns by conducting descriptive and regression analyses on the imputed data and seeing how the estimates differ from those generated from the full sample. Limited Monte Carlo simulation results by using the National Health Nutrition and Examination Survey and Behavioral Risk Factor Surveillance System are presented to demonstrate the effect of each imputation method on reducing bias and increasing efficiency for the parameter estimate of interest for that particular incomplete variable. Although these three methods did not always outperform listwise deletion in our simulated missing patterns, they improved many descriptive and regression estimates when used to impute all incomplete variables at once.

Automated summary

Sample estimates derived from data with missing values may be unreliable due to nonresponse bias, so imputation methods are often preferred. This study compared three popular imputation methods for handling multivariate missing data under different missing patterns. Limited simulation results demonstrated the effect of each imputation method on reducing bias and increasing efficiency for the parameter estimate of interest. Although these methods did not consistently outperform listwise deletion, they improved many descriptive and regression estimates when imputing all incomplete variables at once.