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Activity Number: 380
Type: Contributed
Date/Time: Tuesday, August 4, 2009 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #305194
Title: The Effect of Imputation Misspecification for Incomplete Predictor Time-to-Event Regression Models
Author(s): James Henle and Portia Parker*+ and Nicholas Horton and Shannon McDonough
Companies: Smith College and Smith College and Smith College
Address: , , ,
Keywords: missing data ; incomplete data ; survival analysis ; multiple imputation ; chained equation
Abstract:

Missing predictors are a common problem for regression models of survival outcomes. The use of multiple imputation is a principled methodology for the analysis of incomplete data regression models, though it requires correct specification of the imputation model to yield unbiased estimates of the regression parameters of interest. We study the properties of chained equation imputation models in this setting, and assess the effect of imputation model misspecification. The methods are applied to a cohort study of time to remission to substance abuse.

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