Olà Mirian,
Are you using Poisson models with an offset? I've never tried this
with the Epi package so giving us some sample code and sample data
structure would help.
But if you simply use glm, one way to account for over-dispersion is
by specifying family=quasipoisson. It maximizes the quasilikelihood
rather than the likelihood (don't ask me the maths of this, maybe
start with the Wikipedia...).
Another approach might be hierarchical modelling but we'd need to know
your data structure.
Cheers.
David Evans
-----------------
Epidemiologist, Kappa Santé
Message: 1
Date: Wed, 19 May 2010 10:10:58 -0300
From: "Mirian Carvalho de Souza" <miria...@inca.gov.br>
To: <b...@steno.dk>
Cc: r-sig-epi@stat.math.ethz.ch, Mirian Gmail <mirianc...@gmail.com>
Subject: [R-sig-Epi] Age-period-cohort models for lung cancer
mortality rates
Message-ID: <e587d8fa5fb8444ebc572547cafea...@inca.local>
Content-Type: text/plain; charset="iso-8859-1"
GeleiraHello!
I work at National Cancer Institute, Brazil and I'm interested on
fit age-period-cohort models for lung cancer mortality rates.
I'm using the Epi package, but I have a problem. There is over-
dispersion on my data. How can I deal with this problem? Some one
can suggest a solution?
Thanks
--
Mirian Carvalho de Souza
Instituto Nacional de C?ncer/MS
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