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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 | 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 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 * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/