I would strongly endorse the use of variables of all types: causally prior, 
concomitant, and causally posterior.  They need not be part of the planned 
model.  A good precedent to cite here might be the work of Bob Mislevy at ETS 
in IRT scoring of  cognitive assessments.  They leverage strength from all 
sorts of variables.

-----Original Message-----
From: Impute -- Imputations in Data Analysis 
[mailto:IMPUTE@LISTSERV.IT.NORTHWESTERN.EDU] On Behalf Of Hunsicker, Lawrence
Sent: Monday, April 15, 2013 5:27 PM
To: IMPUTE@LISTSERV.IT.NORTHWESTERN.EDU
Subject: "Accessory" variables in imputation

Good afternoon, all:

A question about the use of "accessory" variables in imputation.  Consider for 
a moment a kidney transplant survival model in which one has data (among other 
things) on peak panel reactive antibody (peak PRA) and the PRA at the time of 
the actual transplant (current PRA).  These actually measure different things, 
but they are obviously strongly correlated.  Data are missing of some fraction 
of these covariates, but most of the time one or the other is available.  
Current PRA is considered to be the stronger predictor of transplant outcomes.  
One is developing a model in which one wants to limit the model df.  So it has 
been decided that the final model will include current PRA but not peak PRA.

I understand that the imputation model must include the outcome variable and 
also all of the covariates that will be used in the final analysis model.  The 
question is whether one can/should include additional covariates (such as peak 
PRA) in the imputation model that WON'T be in the final analysis model.  It 
would seem that inclusion of peak PRA in the imputation model might improve 
considerably the prediction of current PRA, the covariate that will be included 
in the final analysis model.

Is this legitimate?

Thanks in advance to any guidance from the listserv members.

Larry Hunsicker
Prof. Internal Medicine
U. Iowa College of Medicine


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