Volume 53 | Number 3 | June 2018

Abstract List

Yan Ma Ph.D., Wei Zhang Ph.D., Stephen Lyman Ph.D., Yihe Huang M.S.


To identify the most appropriate imputation method for missing data in the State Inpatient Databases () and assess the impact of different missing data methods on racial disparities research.

Data Sources/Study Setting


Study Design

A novel simulation study compared four imputation methods (random draw, hot deck, joint multiple imputation [], conditional ) 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 to retain their hierarchical data structures and missing data patterns. Additional predictive information from the U.S. Census and American Hospital Association () database was incorporated into the imputation.

Principal Findings

Conditional 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.


Conditional substantially improved statistical inferences for racial health disparities research with the .