Use of Sequential Hot-Deck Imputation for Missing Health Care Systems Data for Population Health Research.

ABSTRACT: Electronic medical record (EMR) data present many opportunities for population health research. The use of EMR data for population risk models can be impeded by the high proportion of missingness in key patient variables. Common approaches like complete case analysis and multiple imputation may not be appropriate for some population health initiatives that require a single, complete analytic data set. In this study, we demonstrate a sequential hot-deck imputation (HDI) procedure to address missingness in a set of cardiometabolic measures in an EMR data set. We assessed the performance of sequential HDI within the individual variables and a commonly used composite risk score. A data set of cardiometabolic measures based on EMR data from 2 large urban hospitals was used to create a benchmark

SUBMITTER: Chrenka EA 

PROVIDER: S-EPMC10997447 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature