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From |
Steven Samuels <[email protected]> |

To |
[email protected] |

Subject |
Re: st: RE: ST IPW with OLOGIT |

Date |
Tue, 8 Apr 2008 11:59:33 -0400 |

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, [email protected] 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 <[email protected]>

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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