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Re: st: predicted values in svy glm l(log) f(poisson)


From   Steven Samuels <sjsamuels@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: predicted values in svy glm l(log) f(poisson)
Date   Tue, 28 Dec 2010 15:39:03 -0500

--
Actually, I was wrong. -predictnl- will give you standard errors only for individual contributions to ATT, not for the average. You'll need write a program that incorporates the original -glm- and the creation of the counterfactuals, and outputs the ATT; then -bootstrap- that program. Unless you are lucky enough to be working with a replicate- based data, you'll also have to create your own replicate weights with Stas Kolenikov's -bsweights- program, ("findit bsweights"). However I recommend against using -bsweights- unless you are very familiar with sampling (and bootstrap) theory.

Steve

On Dec 28, 2010, at 2:49 PM, Steven Samuels wrote:

In Stata 10, the easiest way will be to use -predictnl- after -svy: glm-. Please read the FAQ next time. Section 3.3 states:

"The current version of Stata is 11.1. Please specify if you are using an earlier version; otherwise, the answer to your question is likely to refer to commands or features unavailable to you."


Steve

On Dec 28, 2010, at 2:08 PM, Douglas Levy wrote:

Is there a way to do this in Stata 10?

On Thu, Dec 23, 2010 at 4:59 PM, Steven Samuels <sjsamuels@gmail.com> wrote:
Actually, the following code will work whether or not exposure was a stratum
variable at any stage.

Steve

Steven J. Samuels
sjsamuels@gmail.com
18 Cantine's Island
Saugerties NY 12477
USA
Voice: 845-246-0774
Fax:    206-202-4783

**************************CODE BEGINS**************************
sysuse auto, clear
svyset turn [pw= trunk]

replace foreign = foreign +1  //convenient for -margins-

// foreign =2 is the treated group
svy: glm rep78 mpg weight i.foreign, link(log) family(poisson)
margins, subpop(if foreign==2) at(foreign=(1,2)) post vce(unconditional)
// _at2 is foreign as foreign   _at1 is foreign as domestic
lincom _b[2._at]- _b[1._at]  //ATT
margins, coeflegend   //If you forget the coefficient names
lincom _b[2._at] - _b[1bn._at]

***************************CODE ENDS***************************






Use -margins-, but without knowing the survey design it's hard to say more.
Were separate samples taken from the "exposed" and "unexposed" units
(whatever they were)? Were the PSUs stratified by exposure status? Describe
the design and your -svyset- statement.


Steve

On Dec 23, 2010, at 2:03 PM, Douglas Levy wrote:

I am now revisiting this issue, having, with Steve's guidance, settled
on option #2 from my original post. I.e., estimate glm model; predict
daysmissed for exposed=1; predict daysmissed for the exposed group
when exposed is set to 0; take difference of the [weighted] means of
the predictions.

Now my question is, how can I put confidence bounds on the difference
in the mean predictions?

I thank the group for any help it can offer.
Best,
Doug


On Tue, Oct 26, 2010 at 1:34 PM, Steven Samuels <sjsamuels@gmail.com> wrote:

--

Your second suggestion would be an estimate of the average effect of
treatment (exposure, here) among the treated (ATT). For an overview of possibilities, see Austin Nichols's 2010 conference presentations; his 2007 Stata Journal Causal Inference article; and the 2008 Erratum, all linked at
http://ideas.repec.org/e/pni54.html.

Holding covariates at the means in non-linear models can be dangerous.
For an example, see
http://www.stata.com/statalist/archive/2010-07/msg01596.html and Michael N.
Mitchell's followup.

Steve

Steven J. Samuels
sjsamuels@gmail.com
18 Cantine's Island
Saugerties NY 12477
USA
Voice: 845-246-0774
Fax:    206-202-4783

On Oct 26, 2010, at 11:24 AM, Douglas Levy wrote:

I have complex survey data on school days missed for an exposed and
unexposed group. I have modeled the effect of exposure on absenteeism
using svy: glm daysmissed exposure $covariates, l(log) f(poisson). I
would like to estimate adjusted mean days missed for the exposed and
control groups, but I'm not sure of the best way to deal with this in
a non-linear model. There are a couple of methods I've encountered,
and I would be grateful for some thoughts on the pros and cons of
each.

1. Estimate glm model. Reset all covariates to their [weighted] sample
means. Predict daysmissed when exposed=0 and when exposed=1.
2. Estimate glm model. Predict daysmissed for exposed=1. Predict
daysmissed for the exposed group when exposed is set to 0. Take the
[weighted] means of the predictions.
3. Other suggestions?

Thanks.
-Doug
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