Re: st: RE: ST IPW with OLOGIT

[Steven Samuels <sjhsamuels@earthlink.net>]

You are quite right, Joseph; thanks for the correction. I missed - 
glm- in the onscreen help for "svy estimation", because it was listed  
in the "Linear regression" section.

-Steven
On Apr 8, 2008, at 11:18 AM, Jhilbe@aol.com wrote:


> 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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