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Abstract Details
Activity Number:
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159
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Type:
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Topic Contributed
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Date/Time:
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Monday, July 30, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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Health Policy Statistics Section
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Abstract - #304503 |
Title:
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Comparing Regression Coefficients Between Nested Regression Models Subject to Incomplete Data
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Author(s):
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Ofer Harel, Ph.D.*+ and Jun Yan and Chantal Larose
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Companies:
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University of Connecticut and University of Connecticut and University of Connecticut
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Address:
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Department of Statistics, Storrs, CT, 06269,
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Keywords:
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Missing data ;
Multiple imputation ;
Regression ;
Nested models
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Abstract:
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Comparing regression coefficients between nested models is of great practical interest when two explanations of a given phenomenon are specified as linear models. The interest is in whether coefficients of a given set of covariates change significantly when other covariates are added as controls. Methods for such comparison exist for independent and clustered data but all procedures require complete data. We extend the procedure for incomplete data using multiple imputation. We illustrate the problem using a data example.
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Authors who are presenting talks have a * after their name.
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