Population median imputation was noninferior to complex approaches for imputing missing values in cardiovascular prediction models in clinical practice

Objectives: To compare the validity and robustness of five methods for handling missing characteristics when using cardiovascular disease risk prediction models for individual patients in a real-world clinical setting. Study design and setting: The performance of the missing data methods was assessed using data from the Swedish National Diabetes Registry (n = 419,533) with external validation using the Scottish Care Information ? diabetes database (n = 226,953). Five methods for handling missing data were compared. Two methods using submodels for each combination of available data, two imputation methods: conditional imputation and median imputation, and one alternative modeling method, called the naive approach, based on hazard ratios and populations statistics of known risk factors only. The validity was compared using calibration plots and c-statistics. Results: C-statistics were similar across methods in both development and validation data sets, that is, 0.82 (95% CI 0.82-0.83) in the Swedish National Diabetes Registry and 0.74 (95% CI 0.74-0.75) in Scottish Care Information-diabetes database. Differences were only observed after random introduction of missing data in the most important predictor variable (i.e., age). Conclusion: Validity and robustness of median imputation was not dissimilar to more complex methods for handling missing values, provided that the most important predictor variables, such as age, are not missing. (c) 2022 Elsevier Inc. All rights reserved.

Automated summary

This study compared the validity and robustness of five methods for handling missing data in cardiovascular disease risk prediction models. The results showed that median imputation had similar validity and robustness to more complex methods, as long as the most important predictor variables were not missing.