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 ________________________________ Notice: This UI Health Care e-mail (including attachments) is covered by the Electronic Communications Privacy Act, 18 U.S.C. 2510-2521, is confidential and may be legally privileged. If you are not the intended recipient, you are hereby notified that any retention, dissemination, distribution, or copying of this communication is strictly prohibited. Please reply to the sender that you have received the message in error, then delete it. Thank you. ________________________________
