Re: An approach for imputing missing independent variable (covariate)

From: LSheiner <lewis@c255.ucsf.edu> Subject: Re: An approach for imputing missing independent variable (covariate) Date: Thu, 21 Sep 2000 10:04:52 -0700 I may have misunderstood Vladimir's method, but if I did not, then the missing values are simulated with epsilon errors, and the method cannot formally converge. One could substitute the expected values (no noise), and then the algorithm would have the flavor of the EM algorithm, and should converge, but it is not EM, and I am not sure whether it is unbiased. In any event, it would still have the standard error problem. Despite Vladimir's concerns, it is a fact that correctly done multiple imputation can deal with any amount of missing data, and gives unbiased estimates of standard errors because it explicitly measures and adds in the variability caused by not knowing the missing data. Recall that the mult. imput. algorithm computes the estimation variance (square of standard error) as the sum of (i) the covariance of the estimates across the different imputations and (ii) the average covariance from the estimations using the imputed data. The uncertainty due to the lack of the missing data is captured by (i); if one uses the standard error from the last step of the last iteration of Vladimir's method (which I suggested as a possible choice, not he; he made no suggestion regarding SE's), then one would have the too small value, (ii). I again advise anyone facing a serious missing data problem to read the relevant and extensive statistical literature. LBS. -- _/ _/ _/_/ _/_/_/ _/_/_/ Lewis B Sheiner, MD (lewis@c255.ucsf.edu) _/ _/ _/ _/_ _/_/ Professor: Lab. Med., Bioph. Sci., Med. _/ _/ _/ _/ _/ Box 0626, UCSF, SF, CA, 94143-0626 _/_/ _/_/ _/_/_/ _/ 415-476-1965 (v), 415-476-2796 (fax)