st: multiple imputation and propensity score

[Stefano Di Bartolomeo <stefano.dibartolomeo@uniud.it>]

Dear Statalist members

I am doing a study that compares survival after 2 types  of cardiological treatments angioplasty or by-pass. To limit confounding I use a propensity score, calculated as usual by a logistic model. A few covariates had missing values, which I imputed with ICE (5 imputations), separately for each group of treatment. Then I joined again the records in 1 file with 'append' and so have a file with N*6 observations. Then I calculate the propensity score :
mim: logistic angioplasty_vs._bypass  + other_covariates 
Finally I obtain the propensity score with 
mim: predict pscore
As expected, I have 6 sets of propensity scores, one for each set of imputed data (_mj = 1-5) plus the one (_mj = 0)  resulting from the combination of imputed estimators  according to Rubin's rules. Unfortunately, the propensity score of the set _mj = 0 (which is the one I would think correct to use for further analyses) makes no sense, being virtually the same in patients treated with angioplasty or by-pass. The propensity scores of the imputed sets _mj 1-5 instead are ok and distributed as expected in the two treatment groups. I could easily pick up one of this well-working propensity scores for further use, but I know it is not correct. Has anybody ever encountered such a problem? Is it normal that the application of Rubin's rules results in a virtually useless propensity score? If so, how can one properly calculate propensity scores with multiply imputed data-sets?
Thank you so much in advance for your help and suggestions.

Stefano Di Bartolomeo
Udine, Italy
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