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


From   Douglas Levy <douglas_levy@post.harvard.edu>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: predicted values in svy glm l(log) f(poisson)
Date   Tue, 28 Dec 2010 14:08:19 -0500

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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