This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 257
Type: Contributed
Date/Time: Monday, August 2, 2010 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract - #306876
Title: Utilizing Multiple Imputation for Missing Race in the Active Bacterial Core Surveillance (ABCs) System
Author(s): Melissa Lewis*+ and Tracy Pondo and Elizabeth R. Zell
Companies: CDC and CDC and CDC
Address: 1600 Clifton Road NE, Atlanta, GA, 30329-4018,
Keywords: Multiple imputation ; Surveillance ; Race

The Active Bacterial Core surveillance (ABCs) system monitors disease incidence of five bacterial pathogens. Monitoring disease incidence by race is important as incidence can vary by race. Missing race data can lead to underreporting of race-specific disease incidence. Crude approaches to missing data often underestimate variance. A multiple imputation (MI) evaluation was conducted to assess the reliability of multiply imputed race data. We constructed 200 datasets using logistic regression to model missing data patterns. A frequentist evaluation counted the number of datasets with 95% confidence intervals containing the true race-specific incidence. Overall, 97.5% of the confidence intervals contained the true disease incidence within each race category. MI approaches for missing race result in statistically valid inferences that properly reflect uncertainty due to missing values.

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