Hi Rod et al- The within-imputation variance is just the mean of the variances from the M imputed data sets, but Rubin(1987) also gives a formula for the between-imputation variance for the estimate as the variance of the estimates . . ie if your point estimate of interest is Q and you've calculated Qbar as the mean of the M different Q's, the between imputation variance B is 1/(M-1)* sum(Q-Qbar)^2 . . then the total variance associated with Qbar is the average variance estimate plus (1+ 1/M)*B . . .
Although personally I just use SAS's MIanalyze to do all that stuff. But my question - if you're reporting full imputed values, why give the standard deviations, which are too low, instead of the standard errors, which include all the error? Thanks! -Venita > ---------- > From: Rod Little[SMTP:[email protected]] > Sent: Friday, October 22, 2004 9:18 AM > To: Howells, William > Cc: DePuy, Venita; Balasubramani, G.K. ; [email protected] > Subject: RE: [Impute] Multiply Imputation - Descriptive Stats > > If you want to report the means and standard deviations, you can just > average the means and standard deviations from the M imputed data sets. > This is more efficient than reporting values for one data set, and > averaging the imputes before computing the statistics will lead to an > underestimate of the standard deviation (as when conditional means are > imputed). Rod > > On Thu, 21 Oct 2004, Howells, William wrote: > > > We've wondered about this ourselves and I haven't seen it covered in any > > text. We also opted for reporting baseline stats on unimputed data > > because our missing data is mainly in one predictor variable, and > > indicate the observed n in a footnote or the table itself. Bill > > Howells, Wash U Med School, St Louis > > > > > > -----Original Message----- > > From: [email protected] > > [mailto:[email protected]] On Behalf Of DePuy, > > Venita > > Sent: Thursday, October 21, 2004 11:19 AM > > To: 'Balasubramani, G.K. '; ''[email protected]' ' > > Subject: RE: [Impute] Multiply Imputation - Descriptive Stats > > > > Hi Bala et al - > > > > In the varous MI papers we work on in my group, we typically provide > > baseline descriptive stats for the unimputed group. If that is not an > > option, consider using either the first imputed sample or the overall > > imputated values. The overall MI mean for a value is merely the mean of > > the > > 5 (or however many) means, one from each dataset. > > > > However, you typically want to reporta measure of variance. For the > > unimputed or 1st imputed sample method, you can just use std dev. For > > the > > overall imputed values, you need to use standard errors. > > > > Personally, I prefer using unimputed for the baseline descriptives and > > full > > imputation values in subsequent analyses . . . but I would say the main > > deciding factor is the amount of missingness in your data. If it's very > > large, you will probably want to use imputed values. > > > > Hope this helps! > > Venita > > > > -----Original Message----- > > From: Balasubramani, G.K. > > To: '[email protected]' > > Sent: 10/21/2004 12:05 PM > > Subject: [Impute] Multiply Imputation - Descriptive Stats > > > > Hello all, > > > > > > > > This is a basic question in relation to imputation. That is, the imputed > > data is an outcome variable, which is Hamilton depression rating scale. > > I am using the threshold to create an indicator of remission or not > > remission. After I imputed the data (say for 5 times) , how do I show > > the descriptive statistics? That is, the percentage with remission when > > data include imputed values. (Ex. Sex with remission , Employment > > status with remission, etc..). Can I take the mean of the 5 imputed data > > sets to create the indicator variable for remission? Is there any other > > way to present the descriptive using the imputed data? > > > > > > > > Thanks in advance. > > > > > > > > Bala > > > > <<ATT93287.txt>> > > > > _______________________________________________ > > Impute mailing list > > [email protected] > > http://lists.utsouthwestern.edu/mailman/listinfo/impute > > > > _______________________________________________ > > Impute mailing list > > [email protected] > > http://lists.utsouthwestern.edu/mailman/listinfo/impute > > > > > > > > __________________________________________________________________________ > _________ > Roderick Little > Richard D. Remington Collegiate Professor of Biostatistics > U-M School of Public Health Tel (734) 936 1003 > M4045 SPH II Fax (734) 763 2215 > 1420 Washington Hgts email [email protected] > Ann Arbor, MI 48109-2029 http://www.sph.umich.edu/~rlittle/ >
