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From |
Steven Samuels <sjhsamuels@earthlink.net> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: IPW with OLOGIT |

Date |
Mon, 7 Apr 2008 13:31:15 -0400 |

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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**Follow-Ups**:**RE: st: IPW with OLOGIT***From:*"Michael Furukawa" <Michael.Furukawa@asu.edu>

**Re: st: IPW with OLOGIT***From:*Steven Samuels <sjhsamuels@earthlink.net>

**References**:**st: IPW with OLOGIT***From:*"Michael Furukawa" <Michael.Furukawa@asu.edu>

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