--- Mohammed El Faramawi <[email protected]> wrote:
> I am trying to calculate the incidence rate ratio(IRR)
> for a categorical variable ( Mortality) which has
> two categories only ( dead or alive) in a probability
> weighted sample. I am also trying to adjust for
> covariates. I know that poisson regression can be used
> to calculate (IRR) and adjust for covariates given
> that the outcome is a count for example the number of
> deaths, number stroke attacks, etc . My question is
> what regression should I use in such a situation i.e
> outcome which is not count.Can I still use poisson
> regression which I doubt it?? Thank you
--- wangxin wrote:
> so, your dependent variable is dead or alive? that is
> a binary one. you may use Logistic regression.
-logit- will give you odds ratios instead incidence
rate ratios (or risk ratios as they are often called
in case of a binary dependent variable). There are
people who think that odds ratios are too dificult
to understand, for that reason prefer risk ratios or
even risk differences. I disagree. For various views
on this you can follow the thread starting with:
http://www.stata.com/statalist/archive/2008-01/msg00308.html
One problem with risk ratios is that it implies a model
which can result in predicted probabilities larger
than 1 if you have a continuous explanatory variable or
multiple discrete explanatory variables. If you are willing
to live with that, than you can use -poisson- to estimate
this model. Alternatively, you can use -glm- with the
-family(binomial) link(log)- options. The advantage of the
latter command is that it warns you when your model results
in predicted probabilities larger than 1.
Hope this helps,
Maarten
-----------------------------------------
Maarten L. Buis
Department of Social Research Methodology
Vrije Universiteit Amsterdam
Boelelaan 1081
1081 HV Amsterdam
The Netherlands
visiting address:
Buitenveldertselaan 3 (Metropolitan), room Z434
+31 20 5986715
http://home.fsw.vu.nl/m.buis/
-----------------------------------------
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