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

[Douglas Levy <douglas_levy@post.harvard.edu>]

My apologies for not pre-specifying my version.

Reading up on -predictnl-, this gives predictions (and possibly
variances, etc.) for *each observation*. What I'm interested in is
establishing confidence bounds on the difference in the average
predicted values when the exposure is present (=1) and when the
exposure is absent (=0). Would the variance of this difference be the
sum of the variances of the observation-specific predictions?

-Doug

On Tue, Dec 28, 2010 at 2:49 PM, Steven Samuels <sjsamuels@gmail.com> 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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