Re: Missing covariates

From: "Jogarao Gobburu 301-594-5354 FAX 301-480-3212" <GOBBURUJ@cder.fda.gov> Subject: Re: Missing covariates Date: Mon, 02 Jul 2001 11:06:24 -0400 (EDT) Dear Atul, Dr. Schafer presented a tutorial on 'multiple imputation (MI) for missing data problems' at the recent PAGE 2001 meeting in Basel. According to him (originally after Don Rubin), one can use the multivariate (covariate) distribution to simulate the missing values several times and produce the statistics of interest (for eg: the coefficient for the influence of wt on CL and its confidence limits). Though direct experience with this approach is limited and there are no publications in our field (pkpd) to my knowledge, the MI approach seems to be theoretically more appealing than the crude 'median/mean' approach. It might be worthwhile for you to consider using MI and share your findings with the rest of this group. Some related references from other fields: 1. Rubin, DB. Inference and missing data. Biometrika, 63, 581-592 (1976). 2. Schafer JL and Yucel R. Computational strategies for multivariate linear mixed effect models with missing values. J.Computational and Graphical statistics (under review). 3. Lavori, PW. Dawson R. and Shera D. A multiple imputation strategy for clinical trials with truncation of patient data. Stat. in med. 14, 1913-1925, 1995. 4. Schafer JL. Multiple impuation: a primer. Stat.Methods in med. research, 8, 3-15, 1999. Centering is done for convenience, it does not alter statistical inference. Regards, Joga Gobburu Pharmacometrics, CDER, FDA.