st: Is there an -mi drop- command?

[Clyde B Schechter <clyde.schechter@einstein.yu.edu>]

I'm pretty sure the answer to this question is no: I've scoured the MI manual and can't find it.

Let me explain what I'm looking for and why I think such a command would be useful.

Working with a data set that has a great deal of missing information, I sometimes end up over-generating imputations.  That is, my initial estimate of the number of imputations I will need ends up too high.  It would be nice to be able to tell Stata to simply drop some of them, rather than having to start over and generate a smaller number.  You might ask what the harm is in keeping them all.  Well, the more imputations you have, the longer each MI analysis takes, and if there are many analyses, the time starts to add up.

There is another reason I would like to see a command like this.  When the data include difficult patterns of missingness, such as pairs of variables V1 and V2 that are never both non-missing in the same observation (so their imputations are entirely dependent on yet other variables and there is no direct information about their associations), my experience has been that the imputed data sets sometimes contain imputed values that are widely outside the range of the observed data, even by a few orders of magnitude.  MI analyses that include these data sets sometimes produce results that are clearly absurd. 

And there is a third situation.  When doing chained imputations, there is the issue of the burn-in.  Again, we have to make an initial guess and see what happens.  Sometimes, I will generate a large number of imputations using a long burn-in because earlier attempts with a shorter burn-in don't converge to stable estimates.  Maybe I have 75 imputations after a burn-in of 30.  If I study the convergence carefully, I may find that there are 3 or 4 imputations where convergence has not been achieved, but the rest look OK.  It would be nice to be able to tell Stata to drop those 3 or 4--after all, 75 is just a round number and in most circumstances 71 or 72 imputations will serve nearly as well. 

While the -mi estimate- command itself permits specification of a numlist with the numbers of the imputations I want to use, having to repeatedly specify this list is a nuisance.  It would be simpler to be able to drop those imputations, re-save the data set, and then move on to analysis.

The most cogent objection I can think of to this proposal is that perhaps I shouldn't be using MI at all in these situations, or, perhaps, that my imputation models need some serious re-thinking when the imputations show this kind of irregularity.

Any thoughts?

Clyde Schechter
Dept. of Family & Social Medicine
Albert Einstein College of Medicine
Bronx, NY, USA


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