[R] propensity score adjustment using R

[Frank E Harrell Jr]

Bunny, lautloscrew.com wrote:
> Hi all,
> 
> i am looking to built a simple example of a very basic propensity score 
> adjustment, just using the estimated propensity scores as inverse 
> probability weights (respectively 1-estimated weights for the 
> non-treated). As far as i understood, MLE predictions of a logit model 

That is a high variance procedure as compared with covariate adjustment 
using the propensity score, or stratification.

Frank Harrell

> can directly be used as to estimates of the propensity score.
> I already considered the twang package and the several matching 
> approaches and i am basically not trying to reinvent the wheel. Often i 
> could not understand what was going, and why some iterative process like 
> k.stat.max were taking so long.
> Anyway i´d really like to something really simple apart from all this 
> focus on some iterative algorithm thats beyond my scope.
> 
> And here is where the problem starts. Most textbooks i considered 
> proposed to estimate a simple logit model by ML Estimation. Obviously 
> the standard approach to do it using R is glm. The zelig package 
> provides an alternative.  My logit model is as simple at its gets: Y~X, 
> where Y is a treament vector and X is matrix of some covariates.
> 
> I wonder right now if te glm respectively summary(glm(...)) puts out 
> something comparable to ML estimates that can be used as the estimated 
> pscores, in such a way that there is one value for every observation.
> 
> 
> Thanks for any help in advance
> 
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-- 
Frank E Harrell Jr   Professor and Chair           School of Medicine
                      Department of Biostatistics   Vanderbilt University