Journal of Comparative Effectiveness Research Vol. Ahead of print No. | Abstracts

Missing data methods for intensive care unit SOFA scores in electronic health records studies: results from a Monte Carlo simulation

Summary

Missing data can cause problems through decreasing sample size and introducing the potential for introducing bias. This study tested four missing data methods on the Sequential Organ Failure Assessment (SOFA) score, an intensive care research severity adjuster. The simulation study used electronic health record data collected between 2015–2017, where the complete dataset was sampled, missing SOFA score elements imposed and performance examined of four missing data methods – complete case analysis, median imputation, zero imputation (recommended by SOFA score creators) and multiple imputation (MI) – on the outcome of in-hospital mortality. The MI performed well, whereas other methods introduced varying amounts of bias or decreased sample size. The researchers recommend using MI in analyses where SOFA score component values are missing in administrative data research.

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