Brinton Daniel L, Ford Dee W, Martin Renee H, Simpson Kit N, Goodwin Andrew J, Simpson Annie N
College of Health Professions, Medical University of South Carolina, SC 29425, USA.
College of Medicine, Medical University of South Carolina, SC 29425, USA.
J Comp Eff Res. 2022 Jan;11(1):47-56. doi: 10.2217/cer-2021-0079. Epub 2021 Nov 2.
Missing data cause problems through decreasing sample size and the potential for introducing bias. We tested four missing data methods on the Sequential Organ Failure Assessment (SOFA) score, an intensive care research severity adjuster. Simulation study using 2015-2017 electronic health record data, 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. MI performed well, whereas other methods introduced varying amounts of bias or decreased sample size. We recommend using MI in analyses where SOFA score component values are missing in administrative data research.
缺失数据会因样本量减少和引入偏差的可能性而引发问题。我们在序贯器官衰竭评估（SOFA）评分（一种重症监护研究严重程度调整指标）上测试了四种处理缺失数据的方法。利用2015 - 2017年电子健康记录数据进行模拟研究，对完整数据集进行抽样，人为设定SOFA评分元素缺失情况，并检验四种缺失数据方法——完整病例分析、中位数插补、零插补（由SOFA评分创造者推荐）和多重插补（MI）——对院内死亡率这一结局的影响。多重插补表现良好，而其他方法则引入了不同程度的偏差或减小了样本量。我们建议在行政数据研究中，当SOFA评分组件值缺失时，使用多重插补进行分析。
