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Abstract Details
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Activity Number:
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424
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #302151 |
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Title:
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Incomplete Data as a Predictor in Multiple Imputation Models
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Author(s):
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Tracy Pondo*+ and Elizabeth Zell and Melissa M. Lewis
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Companies:
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Centers for Disease Control and Prevention and Centers for Disease Control and Prevention and Centers for Disease Control and Prevention
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Address:
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MS C25, Atlanta, GA, 30329,
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Keywords:
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Multiple Imputation ;
Surveillance ;
Disease ;
Race
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Abstract:
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Multiple imputation of all missing variables in a data set can be performed by sequential regression multivariate imputation to create a complete data set for statistical analysis. Imputations are performed on all variables in the imputation model that have missing values. The imputed values of one variable are used as predictors to impute the values of other variables. An alternative to imputation of all categorical variables in the imputation model is the creation of new categories to represent missing data. To demonstrate the advantages and drawbacks of using incomplete data as a predictor in the imputation model we will compare imputed data sets with and without missing data categories in the imputation model.
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