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Abstract Details

Activity Number: 159
Type: Topic Contributed
Date/Time: Monday, July 30, 2012 : 10:30 AM to 12:20 PM
Sponsor: Health Policy Statistics Section
Abstract - #304503
Title: Comparing Regression Coefficients Between Nested Regression Models Subject to Incomplete Data
Author(s): Ofer Harel, Ph.D.*+ and Jun Yan and Chantal Larose
Companies: University of Connecticut and University of Connecticut and University of Connecticut
Address: Department of Statistics, Storrs, CT, 06269,
Keywords: Missing data ; Multiple imputation ; Regression ; Nested models

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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