The HCUP SID Imputation Project: Improving Statistical Inferences for Health Disparities Research by Imputing Missing Race Data
本研究比较了四种缺失数据插补方法在HCUP州住院数据库中的应用，发现条件多重插补在改善种族健康差异研究的统计推断方面表现最优。
OBJECTIVE: To identify the most appropriate imputation method for missing data in the HCUP State Inpatient Databases (SID) and assess the impact of different missing data methods on racial disparities research. DATA SOURCES/STUDY SETTING: HCUP SID. STUDY DESIGN: A novel simulation study compared four imputation methods (random draw, hot deck, joint multiple imputation [MI], conditional MI) for missing values for multiple variables, including race, gender, admission source, median household income, and total charges. The simulation was built on real data from the SID to retain their hierarchical data structures and missing data patterns. Additional predictive information from the U.S. Census and American Hospital Association (AHA) database was incorporated into the imputation. PRINCIPAL FINDINGS: Conditional MI prediction was equivalent or superior to the best performing alternatives for all missing data structures and substantially outperformed each of the alternatives in various scenarios. CONCLUSIONS: Conditional MI substantially improved statistical inferences for racial health disparities research with the SID.