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Activity Number: 234
Type: Topic Contributed
Date/Time: Monday, August 10, 2015 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #314976 View Presentation
Title: Comparison of Two Approaches for Imputing a Composite Categorical Variable
Author(s): Yi Pan* and Ruiguang Song and Yulei He and Mi Chen
Companies: CDC and CDC and CDC and CDC
Keywords: Multiple Imputation ; Composite ; Categorical Variable
Abstract:

Missing data is a common problem in many data systems. Variables with missing values are often binary or categorical and the missing pattern can be arbitrary. For example, there is an increasing trend in missing transmission category among HIV cases reported to CDC through the National HIV Surveillance System (NHSS) in the United States. Transmission category (categorical) summarizes the multiple risk factors (binary) that an individual may have had by hierarchically selecting the one through which HIV was most likely transmitted. Accurate estimation of the distribution of transmission category among persons with diagnosed HIV infection is critical for the adequate allocation of HIV prevention and care resources. Multiple imputation is a popular approach to analyses with missing data. There are two ways to impute transmission category. One is directly imputing missing values of the transmission category variable, and the other is imputing missing values of binary risk factors first and then computing transmission category based on the imputed risk factors. In this study, the performance of the two imputation methods is examined through simulation and application to NHSS data.


Authors who are presenting talks have a * after their name.

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