Missing Data in OHCA Registries: How Multiple Imputation Methods Affect Research Conclusions-Paper II.

ABSTRACT: Background/Objectives: Missing data in clinical observational studies, such as out-of-hospital cardiac arrest (OHCA) registries, can compromise statistical validity. Single imputation methods are simple alternatives to complete-case analysis (CCA) but do not account for imputation uncertainty. Multiple imputation (MI) is the standard for handling missing-at-random (MAR) data, yet its implementation remains challenging. This study evaluated the performance of MI in association analysis compared with CCA and single imputation methods. Methods: Using a simulation framework with real-world Singapore OHCA registry data (N = 13,274 complete cases), we artificially introduced 20%, 30%, and 40% missingness under MAR. MI was implemented using predictive mean matching (PMM), random for

SUBMITTER: Zhan SJ 

PROVIDER: S-EPMC12842346 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature