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Missing Data in OHCA Registries: How Imputation Methods Affect Research Conclusions-Paper I
Stella Jinran Zhan1, Seyed Ehsan Saffari1, Marcus Eng Hock Ong2
1Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore 169857, Singapore.
Journal of Clinical Medicine
|September 13, 2025
Summary
Complete-case analysis is suboptimal for missing data in out-of-hospital cardiac arrest (OHCA) research. K-Nearest Neighbours (KNN) and missingness-indicator (MxI) imputation methods offer better alternatives to reduce bias in observational studies.
Area of Science:
- Medical Informatics
- Biostatistics
- Public Health
Background:
- Missing data is a common challenge in clinical observational studies, particularly in time-sensitive emergency registries like out-of-hospital cardiac arrest (OHCA).
- Complete-case analysis (CCA), often used in observational studies, can lead to biased results and reduced representativeness.
- Effective handling of missing data is crucial for accurate association analysis in multi-national registries.
Purpose of the Study:
- To evaluate the impact of various single imputation methods on association analysis within OHCA registries.
- To compare the performance of statistical and machine learning (ML) single imputation techniques against CCA.
- To assess the reliability of conclusions drawn from OHCA research when employing different missing data handling strategies.
Main Methods:
- Utilized a complete dataset (N=13,274) from the Pan-Asian Resuscitation Outcomes Study (PAROS) registry (2016-2020) as a reference.
- Intentionally introduced missing values under a Missing At Random (MAR) mechanism into selected variables.
- Compared complete-case analysis (CCA) with single imputation methods, including K-Nearest Neighbours (KNN) and missForest, to analyze the association between bystander cardiopulmonary resuscitation (BCPR) and mobile app alerts, adjusting for confounders.
Main Results:
- Complete-case analysis (CCA) demonstrated suboptimal performance, yielding more biased estimates and wider confidence intervals compared to single imputation methods.
- The missingness-indicator (MxI) method provided a balance between bias reduction and implementation simplicity.
- K-Nearest Neighbours (KNN) imputation outperformed other methods, while missForest introduced bias in specific scenarios.
Conclusions:
- K-Nearest Neighbours (KNN) and missingness-indicator (MxI) imputation are practical and superior alternatives to CCA for mitigating bias in observational studies.
- The selection of appropriate imputation methods is vital for ensuring reliable findings in OHCA research.
- This study's findings have significant implications for improving data analysis in various registries facing missing data challenges.
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