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Activity Number: 294
Type: Contributed
Date/Time: Tuesday, July 31, 2012 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #305939
Title: The Implications of Software Choice for Handling Missing Data in the Presence of Interaction Effects Involving at Least One Categorical Variable
Author(s): Aya A Mitani*+ and Allison W Kurian and Amar K Das and Manisha Desai
Companies: Stanford University and Stanford University and Stanford University and Stanford University
Address: 642 Chenery St, SAN FRANCISCO, CA, 94131, United States
Keywords: Missing data ; Multiple imputation ; Interaction effects ; Categorical predictors ; SAS ; STATA

Multiple imputation of interaction terms can be challenging. Two main methods include passive imputation, which places constraints on the interaction term given the main effects, and active imputation, which treats the interaction term independently from the main effects. In his study examining the bias resulting from packages including SAS procedures that employ both methods based on a joint modeling (JM) approach, Von Hippel (2009) found that active imputation leads to less biased estimates. When missing variables include nominal categorical variables, however, a fully conditional specification (FCS) approach may be preferred over JM. Through simulations, we compare properties of estimates resulting from FCS-based imputation among SAS's active, and STATA's active and passive imputation methods. We provide guidelines for making sensible analysis choices based on our findings, and illustrate differences among the choices using real data from a breast cancer study.

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