[R] propensity score adjustment using R
Frank E Harrell Jr
f.harrell at vanderbilt.edu
Thu Sep 18 17:56:52 CEST 2008
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.
> 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
> R-help at r-project.org mailing list
> PLEASE do read the posting guide
> and provide commented, minimal, self-contained, reproducible code.
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
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