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Re: st: multiple imputation and propensity score

From   Stas Kolenikov <[email protected]>
To   [email protected]
Subject   Re: st: multiple imputation and propensity score
Date   Wed, 24 Aug 2011 12:48:47 -0500

On Wed, Aug 24, 2011 at 11:39 AM, Stefano Di Bartolomeo
> In truth I am trying to be humble and apply the best methodology I can. I got tricked into this problem in 2 simple steps. First I read  'A Guide to Imputing Missing Data with Stata by Mark Lunt', which is a step by step guide for non-pundits like me. Throughout the guide a propensity score is the main goal of the examples. So I got the feeling that multiple imputation is good for propensity score and did that. Then, I reviewed the recent literature on propensity scores and it seems that matching is the technique that most reduces bias as compared to stratification on quintiles  or inclusion of PS as covariate. And again, tried to follow the suggestion. Now I understand I have to give up one of the two techniques.

I believe you could still see through your approach with both MI and
PS. For that, you would need:

1. create multiple imputations using -ice- or the official -mi-.
2. write your own estimation program (say you named it -mi_ps_st-) that would
2a. run logistic regression as a matter of propensity score modeling
2b. generate propensity scores
2c. run your survival model
2d. Ideally, you'd want to correct the standard errors in the survival
model for the fact that you have created some of the regressors. It is
possible to do that in the linear regression context (see Hardin
(2002,, but I
don't know if this approach is generalizable to -streg-.
3. run your -mi_ps_st- prefixed by -mim- (or, respectively, -mi
estimate-) to combine the estimates and standard errors. Remember that
MI only makes sense when you have the final parameter estimates and
their standard errors. The intermediate results, like specific
imputations, or observation-level averages across them, as you thought
initially for your propensity scores, may not be very meaningful.

The guide you referred to is dated, in the sense that Stata 12
incorporates MICE methodology in the official -mi-. The guide would
still be applicable to Stata 11. I also did not like it relying on the
author's written programs, although that is sometimes inevitable (I
tend to trust the stuff that underwent some minimal checks at SJ or
SSC a little bit better).

BTW, I don't think it is at all possible to get the right standard
errors from matching, so you would probably have to let that
methodology go, anyway. So you would have to look into other options
with your survival model.

Stas Kolenikov, also found at
Small print: I use this email account for mailing lists only.

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