JSM 2005 - Toronto

Abstract #304049

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 198
Type: Contributed
Date/Time: Monday, August 8, 2005 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract - #304049
Title: To Impute or Weight: Handling Missing Data in Complex Epidemiologic Survey
Author(s): Hoang T. Nguyen*+
Companies: The University of Texas Medical Branch
Address: School of Nursing, 301 University Blvd., Galveston, TX, 77555-1029, United States
Keywords: Multiple Imputation ; Reweighting ; Survey Analysis ; NHANES ; Missing data
Abstract:

Missing data is inevitable in large survey. Two strategies for handling missing data are multiple imputation and reweighting. Multiple imputation is a process where missing data are replaced with plausible values based on the existing data. In contrast, reweighting drops the cases with missing data and assigns larger weights to the cases with complete data, thereby maintaining the population size. Both strategies require extensive resources and time, therefore it is important to examine when these strategies should be employed. This study compared the two approaches using data from The Third National Health and Nutrition Examination Survey (NHANES III). Analyses were conducted with SAS (version 9) and SAS-callable SUDAAN (version 9).

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