re: re:st: pscore question

[Alistair Windsor <alistair.windsor@gmail.com>]

Propensity score reweighting is a possibility but often it does not 
adequately correct for the covariate imbalance. See for example 
"Propensity Score Analysis: Statistical Methods and Applications" by Guo 
and Fraser. Reweighting does allow easy use of regression for further 
analysis unlike methods like full matching.

Associated to psmatch2 is pstest which looks at covariate imbalance 
after matching. I also like to run logit/probit regressions on the 
treatment over the combined treated and pseudo control groups and look 
for joint vanishing of the non-constant terms using a Wald or likelihood 
ratio test.

One further option is to look into optmatch for R. This performs optimal 
matching using a variety of schemes.

The best bet is to try several techniques and see which work best for 
your purposes.

Yours,

Alistair



On 2/10/11 1:33 AM, statalist-digest wrote:
> Date: Wed, 9 Feb 2011 10:23:19 -0500
> From: "Ariel Linden. DrPH"<ariel.linden@gmail.com>
> Subject: re:st: pscore question
>
> Daniel,
>
> If you already have a propensity score estimated, you can use psmatch2" (a
> user written program by Edwin Leuven and Barbara Sianesi). This program
> allows you to use an existing propscore and it will conduct the necessary
> tests of balance on covariates.
>
> You also asked about blocking - there is another program that naturally
> "blocks", but is flexible enough to allow you to generate your own blocks.
> This program is called "cem" [Coarsened Exact Matching], a user-written
> program by Matthew Blackwell and Gary King at Harvard.
>
> Given what you are trying to do, I would probably prefer to use propensity
> score-based weighting over matching. Austin Nichols wrote a nice paper on
> that " Erratum and discussion of propensity-score reweighting" in
> Stata Journal  8(4):532--539
>
> I hope this helps, and good luck!
>
> Ariel

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