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Empirical Comparison of Imputation Methods for Multivariate Missing Data in Public Health
1Department of Biostatistics and Epidemiology, University of Oklahoma Health Sciences Center, 801 NE 13th St, Oklahoma City, OK 73104, USA.
International Journal of Environmental Research and Public Health
|January 21, 2023
Summary
Imputation methods like sequential multiple imputation can improve estimates from incomplete data. These techniques are often better than listwise deletion for handling missing values in research datasets.
Area of Science:
- Statistics
- Data Science
- Biostatistics
Background:
- Missing data can lead to unreliable estimates and nonresponse bias, affecting population inferences.
- Imputation is frequently preferred over listwise deletion for addressing multivariate missing data.
- Accurate handling of missing data is crucial for valid statistical analysis.
Purpose of the Study:
- To compare the performance of three imputation methods: sequential multiple imputation, fractional hot-deck imputation, and generalized efficient regression-based imputation.
- To evaluate these methods in handling multivariate missingness across various missing data patterns.
- To assess the impact of imputation on descriptive and regression estimates compared to full-sample data.
Main Methods:
- Descriptive and regression analyses were performed on data imputed using sequential multiple imputation, fractional hot-deck imputation, and generalized efficient regression-based imputation.
- Monte Carlo simulations were conducted using data from the National Health Nutrition and Examination Survey and Behavioral Risk Factor Surveillance System.
- The study examined bias reduction and efficiency gains for parameter estimates of incomplete variables.
Main Results:
- The compared imputation methods did not consistently outperform listwise deletion across all simulated missing patterns.
- However, these imputation techniques notably improved many descriptive and regression estimates when applied to all incomplete variables simultaneously.
- The study demonstrated the effect of each imputation method on bias and efficiency for specific incomplete variables.
Conclusions:
- Sequential multiple imputation, fractional hot-deck imputation, and generalized efficient regression-based imputation offer improvements for handling multivariate missing data.
- While not universally superior to listwise deletion in all simulated scenarios, these imputation methods enhance estimates when applied comprehensively.
- Researchers should consider these imputation strategies to improve the reliability of analyses with incomplete datasets.
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