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

From   Douglas Levy <[email protected]>
To   [email protected]
Subject   st: predicted values in svy glm l(log) f(poisson)
Date   Tue, 26 Oct 2010 11:24:06 -0400

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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