Related Experiment Video
Updated: Dec 13, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
A passive and inclusive strategy to impute missing values of a composite categorical variable with an application to
Yi Pan1, Yulei He2, Ruiguang Song1
1Division of HIV/AIDS Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, GA.
Purpose:
Multiple imputation (MI) is a widely acceptable approach to missing data problems in epidemiological studies. Composite variables are often used to summarize information from multiple, correlated items. This study aims to assess and compare different MI methods for handling missing categorical composite variables.
Methods:
We investigate the problem in the context of a real application: estimating the prevalence of HIV transmission category, which is a composite variable generated by applying a hierarchical algorithm to a group of binary risk source variables from a national program data set. We use simulation studies to compare and assess the performance of alternative MI strategies. These methods include the active imputation, just another variable, and the passive imputation approaches.
Results:
Our study suggests that the passive imputation approach performs better than the direct imputation approach and the inclusive and general imputation model (i.e. passive imputation with interactions) performs the best. There is no need to embed the information from the variable-combining algorithm in the passive imputation modeling.
Conclusion:
We recommend practitioners adopting an inclusive and general passive imputation modeling strategy.
Related Concept Videos
Bias in Epidemiological Studies
Statistical Methods for Analyzing Epidemiological Data
How Data are Classified: Categorical Data
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
Strategies for Assessing and Addressing Confounding
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Contingency Table
Causality in Epidemiology

