Re: st: Combining multiple imputation with propensity score matching

[David Kantor <kantor.d@att.net>]

Hi.
As the author of mahapick, I would like to mention that, indeed, it 
does not pick unique matches. (This could be an avenue for future development.)
You can specify that it generates a multitude of match candidates, 
which is virtually a queue, in order of closeness, of possible 
matches for each primary ("treated") case. You then can take this and 
run a loop that visits primary cases in a random order. For each such case,
 select the best candidate for the given primary case;
 remove that selected match as a candidate for use in later passes 
through the loop.

I recommend that if you want more than one match (say 3) per primary 
case, that you run this loop several (3) times (maintaining the same 
data structure that disqualifies candidates from future matching) -- 
rather than selecting, say, the best 3 matches for each case in one 
pass through the loop. The latter method might enable earlier cases 
in the loop to grab better matches.

Of course, this has a random element to the process. You may or may 
not like that. But you need some way of deciding who gets a given 
candidate if it is matched to more than one primary case.

I had done this selection process once, several years ago; I might be 
able to dig up the code if necessary. My co-worker also had a plan to 
somehow optimize the process by swapping matches in order to minimize 
the sum of the distances. That was too complex to be done in Stata, 
and we abandoned it. I understand that the task was taken up by 
others (in C, I suppose), but the result was no better than the 
original random process.

HTH
--David

At 11:17 AM 3/2/2010, John E. Cornell wrote:

> Dear Stata Folks:
>
> I have a large, and somewhat complicated multi-site dataset, that 
> requires the use of multiple imputation to fill-in missing lab 
> values that I need to generate propensity scores for three classes 
> of drugs. I used the new multiple imputation procedure based on 
> multivariate normal regression to fill-in the missing lab values. We 
> created 20 imputed datasets if the flong format, and used logistic 
> regression to compute and save the propensity scores in logit form 
> within each imputed set. We used mahapick to select to match cases 
> (being on one or more of the three agents) to controls (never on any 
> of the three agents). This worked well, but there are two problems 
> we encountered at this stage. First, the procedure selects the 
> closest match actual distance may be very large so we needed to edit 
> the matches to maintain a subset of cases with reasonable closeness. 
> Second, the procedure may match the same control to more than one 
> case, so we needed to restrict the sample to unique matches. 
> Finally, the number of matches varied between imputed sets.
>
> It does not appear that the mi estimate command can handle this 
> situation. So, we are left with the prospect of writing our own code 
> to compute and combine the model estimates. We are relatively novice 
> Stata programmers at the moment, and we would welcome any 
> suggestions, references, etc. that the Stata community could provide 
> that will help us solve this problem.
>
> Cheers,
>
>
> John E. Cornell, Ph.D.
> Professor
> Department of Epidemiology and Biostatistics
> University of Texas Health Science Center, San Antonio
> 7703 Floyd Curl Drive
> San Antonio, Texas 78229-3900
> [...]

*
*   For searches and help try:
*   http://www.stata.com/help.cgi?search
*   http://www.stata.com/support/statalist/faq
*   http://www.ats.ucla.edu/stat/stata/