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

Abstract Details

Activity Number: 297
Type: Contributed
Date/Time: Tuesday, August 3, 2010 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #308557
Title: Multiple Imputation for Regression Models with Interaction
Author(s): Soeun Kim*+ and Thomas R. Belin and Catherine A. Sugar
Companies: University of California, Los Angeles and University of California, Los Angeles and University of California, Los Angeles School of Public Health
Address: 11863 Texas Ave #302, Los Angeles, CA, 90025,
Keywords: missing data ; multiple imputation ; regression ; interaction
Abstract:

Inference based on data from experiments or surveys is often complicated due to incomplete data which can lead to bias in the results. Standard missing data procedures do not reflect possible interactions in regression relationships among variables. We consider a regression model with two continuous predictors and an interaction term where one of the covariates may be missing. We derive the conditional distribution of the missing covariate given the observed covariate and the outcome variable, assuming a multivariate normal distribution among the covariates. We conduct a simulation study in order to evaluate the performance of inference for regression parameters. The results suggest standardizing the variables before imputation and rescaling afterwards.


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