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

[Steven Samuels <sjsamuels@gmail.com>]

--

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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