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

From   Steven Samuels <[email protected]>
To   [email protected]
Subject   Re: st: predicted values in svy glm l(log) f(poisson)
Date   Tue, 26 Oct 2010 13:34:07 -0400


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

Holding covariates at the means in non-linear models can be dangerous. For an example, see and Michael N. Mitchell's followup.


Steven J. Samuels
[email protected]
18 Cantine's Island
Saugerties NY 12477
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

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?

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