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


From   Douglas Levy <douglas_levy@post.harvard.edu>
To   statalist@hsphsun2.harvard.edu
Subject   st: predicted values in svy glm l(log) f(poisson)
Date   Thu, 23 Dec 2010 14:03:12 -0500

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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