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A comparison of various imputation algorithms for missing data
Jürgen Kampf1, Iryna Dykun1, Tienush Rassaf1
1Department of Cardiology and Vascular Medicine, University Hospital of Essen, Essen, Germany.
Plos One
|May 12, 2025
Summary
Predictive mean matching is the best subroutine for one-dimensional imputations in multiple imputation by chained equations, offering superior statistical performance for missing data compared to other methods, excluding weighted predictive mean matching due to computational time.
Area of Science:
- Statistics
- Biostatistics
- Data Science
Background:
- Incomplete datasets are common in medical and scientific research.
- Missing data requires robust imputation methods for valid analysis.
Purpose of the Study:
- To compare various imputation subroutines for one-dimensional imputations within multiple imputation by chained equations.
- To evaluate the statistical performance and computational efficiency of different imputation algorithms.
Main Methods:
- Comparison of imputation subroutines: predictive mean matching, weighted predictive mean matching, sampling, classification/regression trees, and random forests.
- Evaluation on both real-world (cardiac disease survival data) and simulated datasets.
- Assessment of statistical properties (bias, MSE, coverage) and computation time for linear, logistic, and Cox regression models.
Main Results:
- Weighted predictive mean matching was excluded due to excessive computation time.
- Predictive mean matching generally demonstrated the best statistical performance across various tested scenarios.
- The study represents the largest comparison of multiple imputation by chained equations subroutines to date.
Conclusions:
- Predictive mean matching is a highly effective subroutine for one-dimensional imputation in multiple imputation by chained equations.
- Careful selection of imputation subroutines is crucial for accurate statistical inference with incomplete data.
- This research provides valuable guidance for handling missing data in complex statistical modeling.
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