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

[Steven Samuels <sjsamuels@gmail.com>]

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
> *
> *   For searches and help try:
> *   http://www.stata.com/help.cgi?search
> *   http://www.stata.com/support/statalist/faq
> *   http://www.ats.ucla.edu/stat/stata/
>
> *
> *   For searches and help try:
> *   http://www.stata.com/help.cgi?search
> *   http://www.stata.com/support/statalist/faq
> *   http://www.ats.ucla.edu/stat/stata/

*
*   For searches and help try:
*   http://www.stata.com/help.cgi?search
*   http://www.stata.com/support/statalist/faq
*   http://www.ats.ucla.edu/stat/stata/

*
*   For searches and help try:
*   http://www.stata.com/help.cgi?search
*   http://www.stata.com/support/statalist/faq
*   http://www.ats.ucla.edu/stat/stata/