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Re: st: multiple imputation and propensity score


From   Stefano Di Bartolomeo <stefano.dibartolomeo@uniud.it>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: multiple imputation and propensity score
Date   Wed, 24 Aug 2011 18:39:15 +0200

Thank you very much, Maarten.

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. 
As a last resort: do you think that using just one of the imputed datasets for matching + further analyses would be grossly inappropriate? I have missing data for only  four of the about twenty variables that go into the propensity model and I impute them more for not loosing cases than to have better estimates.

Thanks a lot again,
stefano

Il giorno 24/ago/2011, alle ore 17.31, Maarten Buis ha scritto:

> On Wed, Aug 24, 2011 at 5:03 PM, Stefano Di Bartolomeo wrote:
>> As you supposed, after calculating the PS I do some 1:1 matching and then I run several models of Cox regression for various outcomes, stratified on matched pairs. I am very curious to learn if all this would be feasible carrying forward all the imputed data sets. For sure, it is beyond my skills. I vaguely surmise that perhaps it could be possible if I used logistic regression instead of Cox regression, but I am not sure and in any case I must use survival analysis. Moreover, how could I draw a graphic of the density distribution of PS in the two groups before and after matching without having a unique PS?
> 
> Propensity score matching and multiple imputation are two techniques
> that are both complicated, difficult to get right and easy to get
> wrong and have their own set of non-trivial possibly conflicting(*)
> assumptions. I would just focus on doing one of them right rather than
> trying to stack techniques this way.
> 
> Hope this helps,
> Maarten
> 
> (*) I don't know if that is the case, but it would not surprise me.
> --------------------------
> Maarten L. Buis
> Institut fuer Soziologie
> Universitaet Tuebingen
> Wilhelmstrasse 36
> 72074 Tuebingen
> Germany
> 
> 
> http://www.maartenbuis.nl
> --------------------------
> 
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