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

 From Steven Samuels To statalist@hsphsun2.harvard.edu Subject Re: st: predicted values in svy glm l(log) f(poisson) Date Tue, 28 Dec 2010 14:49:03 -0500

```--

```
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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