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st: RE: ST IPW with OLOGIT


From   Jhilbe@aol.com
To   statalist@hsphsun2.harvard.edu
Subject   st: RE: ST IPW with OLOGIT
Date   Tue, 8 Apr 2008 11:18:51 EDT

You state that the -glm- command does not use survey statistics; ie the  
-svy- prefix. I do not believe that this is the case. The survey manual (page 2)  
indicates that glm takes -svy-, and I recall using it several times  for 
various projects. Every attempt has been made to make the -glm- command  compatible 
with all ML, stepwise, and survey options. 
 
Joseph Hilbe
 
 
 
Date: Mon, 7 Apr 2008 13:31:15 -0400
From: Steven Samuels  <sjhsamuels@earthlink.net>
Subject: Re: st: IPW with  OLOGIT

Michael,

I am not expert in this area. That said, your post  is confusing to me.

• I don't see why you want to use -ologit-.  The  important part of a  
treatment selection model is to model the  probability of selection  
into a treatment group.  The outcomes of  treatments might be ordered  
by treatment, but there is no reason to  assume a priori that the  
selection probabilities should be ordered by  treatment number.

• You state you have a count outcome, but your  specification for the - 
glm- command is for a binary outcome.

• The  -glm- command does not take a -svy- prefix.  Try "help   
svy_estimation" for a list of commands that will take survey weights   
and design factors.

•To use a survey weight, you would need to  -svyset- your data after  
creating your new weights; then you would  need to use the -svy-  
prefix for your command.

• Wooldridge's  example of treatment selection is for the purpose of  
estimating  individual population means; then taking the difference  
between those  means. For that purpose you would need to define three  
weights ipw1,  ipw2, ipw3 based on p1, p2, and p3. (See help for - 
generate-) Run your  second regression model three times, each with a  
different weight, you  would average the predicted values over the  
entire sample. The  post-estimation command -predictnl- might compute  
the difference  between means.

• Wooldridge's method would estimate a difference  between   
populations means unadjusted for covariates.  Is  this what you want?

• Apparently Wooldridge's doubly-robust  variance-matrices take into  
account variability due to computing the  propensity scores, although  
I don't quite follow the argument .   You could also bootstrap or  
jackknife the entire process.  As  sums are over the entire sample,  
then an estimated contrast in means  is an average of the contrast in  
the predicted values of individual  predictions.


• As you have the same variables ("$var") on the right  hand side of  
your treatment and outcome equations, I don't see a need  for the IPW  model 
at all.


- -Steven

> I am trying to  adjust for selection on observables using propensity
> scores as inverse  probability weights (ipw), following Wooldridge  
> ("IPW
>  Estimation for General Missing Data Problems").  My dataset has a
>  complex survey design with survey weights (svywt), and I want to  
>  adjust
> for selection bias of an ordered treatment (t1,t2,t3) on a  count
> outcome.  Can someone help me with the Stata code to compute  the IPW
> using the predicted probabilities as propensity scores?  I  want to
> compute IPW by multiplying the survey weight*(1/propensity  score),
> following Zanutto et al. (2005).
>
> ologit t_cat  $var
> predict p1 p2 p3
>
> /*Here's where I need  help*/
> ipw=svywt*[1/p1 ...]
>
> glm depvar $var t2 t3  [pweight=ipw], fam(bin) link(logit) irls robust
>
>
> Thanks  in advance,
> Mike

> Michael F. Furukawa, PhD
> Assistant  Professor
> Health Management and Policy
> W. P. Carey School of  Business
> Arizona State University
> (480)  965-2363

------------------------------
 



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