If a conditional mean is imputed, it is better not to condition on Y. If a draw is imputed, it is better to condition on Y. The latter is the approach in multiple imputation, and is recommended. For more discussion see Little (1992 JASA) Rod Little.
On Thu, 23 May 2002, Craig D. Newgard wrote: > Constantine, > Generally, I agree with your colleague. The danger in using an outcome > variable in the imputation process is in creating associations between > predictors and the outcome that would not be present otherwise. If this > occurs (essentially creating confounding variables), your result could be > biased. It is difficult to tell whether or not this occurs when the outcome > is included in the imputation process, as differences in results between the > non-imputed data and the imputed data could reflect a correction of the bias > in using data restricted to non-missing values (the non-imputed dataset) or > bias created from the imputation process, or both. The simplest way to deal > with this is to leave out the outcome variable from the imputation process, > as a benefit in precision may be offset by a loss of validity. > > Craig > > Craig D. Newgard, MD, MPH > Research Fellow > Department of Emergency Medicine > Harbor-UCLA Medical Center > 1000 West Carson Street, Box 21 > Torrance, CA 90509 > (310)222-3666 (Office) > (310)782-1763 (Fax) > [EMAIL PROTECTED] > > > -----Original Message----- > From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]]On > Behalf Of Constantine Daskalakis > Sent: Wednesday, May 22, 2002 1:18 PM > To: [EMAIL PROTECTED] > Cc: Constantine Daskalakis > Subject: IMPUTE: imputing covariates > > > Hi. > > I have a regression of Y on a bunch of Xs (always observed) and on Z > (sometimes missing). > > The X's will be used to impute Z. But should Y also be used in imputing Z? > > My reading of the literature suggests that's not a problem and can often be > a good thing in terms of gaining precision. A colleague argues that using > the outcome to impute the predictor, will bias the estimated effect of that > predictor in the main regression model. She argues that, by using Y, > "you're stacking the deck, so to speak", ie, the imputation determines what > you'll find out in the main regression model. > > Is there a heuristic response to that concern? > (Or, if I'm wrong, please someone correct me!) > > Thanks, > cd > > PS Always assuming MAR of Z (ie, missingness of Z does not depend on the > unobserved Z itself). > > > > ________________________________________________________________ > > Constantine Daskalakis, ScD > Assistant Professor, > Biostatistics Section, Thomas Jefferson University, > 125 S. 9th St. #402, Philadelphia, PA 19107 > Tel: 215-955-5695 > Fax: 215-503-3804 > Email: [EMAIL PROTECTED] > Webpage: http://www.kcc.tju.edu/Science/SharedFacilities/Biostatistics > > > > > ___________________________________________________________________________________ Roderick Little Richard D. Remington Collegiate Professor (734) 936-1003 Department of Biostatistics Fax: (734) 763-2215 U-M School of Public Health M4045 SPH II [EMAIL PROTECTED] 1420 Washington Hgts http://www.sph.umich.edu/~rlittle/ Ann Arbor, MI 48109-2029
