I would say that it all depends. In Hunsicker's example, peak PRA sounds like it was excluded from the outcome space because of colinearity issues. This makes it an ideal adjunct variable to the imputation process.
--Dave Judkins Sent from my iPhone On Apr 15, 2013, at 7:13 PM, "Paul von Hippel" <paulvonhip...@yahoo.com<mailto:paulvonhip...@yahoo.com>> wrote: Let me correct my first sentence: What I meant to say is that Meng showed that MI imputation is still valid of auxiliary variables have been included in the imputation model. So it's a legitimate practice and, if its' not too much trouble, why not. But it probably won't make much difference. ________________________________ From: Paul von Hippel <paulvonhippel.utaus...@gmail.com<mailto:paulvonhippel.utaus...@gmail.com>> To: IMPUTE@LISTSERV.IT.NORTHWESTERN.EDU<mailto:IMPUTE@LISTSERV.IT.NORTHWESTERN.EDU> Sent: Monday, April 15, 2013 4:39 PM Subject: Re: "Accessory" variables in imputation Meng showed that MI imputation is still valid if auxiliary variables have been included in the analysis. In theory auxiliary variables can improve the estimates, but in practice they rarely help much. See the recent paper by Sarah Mustillo in Sociological Methods & Research. On Mon, Apr 15, 2013 at 4:27 PM, Hunsicker, Lawrence <lawrence-hunsic...@uiowa.edu<mailto:lawrence-hunsic...@uiowa.edu>> wrote: 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. ________________________________ -- Best wishes, Paul von Hippel Assistant Professor LBJ School of Public Affairs Sid Richardson Hall 3.251 University of Texas, Austin 2315 Red River, Box Y Austin, TX 78712 (512) 537-8112 ________________________________ This message may contain privileged and confidential information intended solely for the addressee. Please do not read, disseminate or copy it unless you are the intended recipient. If this message has been received in error, we kindly ask that you notify the sender immediately by return email and delete all copies of the message from your system.
