On 07/30/2014 05:00 AM, r-help-requ...@r-project.org wrote:
A while ago, I inquired about fitting excess relative risk models in R. This is
a follow-up about what I ended up doing in case the question pops up again.
While I was not successful in using standard tools, switching to Bayesian
(stanFit)
stanFit
-Original Message-
From: Wollschlaeger, Daniel
Sent: Thursday, January 9, 2014 10:44 AM
To: David Winsemius
Cc: r-help@r-project.org
Subject: RE: AW: [R] Linear relative rate / excess relative risk models
Thanks for your suggestions! Here are links to simulated data
event and offset pyears.
Many thanks, D
-Original Message-
From: David Winsemius [mailto:dwinsem...@comcast.net]
Sent: Thursday, January 09, 2014 4:33 AM
To: Wollschlaeger, Daniel
Cc: r-help@r-project.org
Subject: Re: AW: [R] Linear relative rate / excess relative risk models
My question is how I can fit linear relative rate models (= excess relative
risk models, ERR) using R. In radiation epidemiology, ERR models are used to
analyze dose-response relationships for event rate data and have the following
form [1]:
lambda = lambda0(z, alpha) * (1 + ERR(x, beta))
*
I would fit a Poisson model to the dose-response data with offsets for the
baseline expecteds.
Sent from my iPhone
On Jan 8, 2014, at 10:49 AM, Wollschlaeger, Daniel
wollschlae...@uni-mainz.de wrote:
My question is how I can fit linear relative rate models (= excess relative
risk
Von: David Winsemius [dwinsem...@comcast.net]
Gesendet: Mittwoch, 8. Januar 2014 19:06
An: Wollschlaeger, Daniel
Cc: r-help@r-project.org
Betreff: Re: [R] Linear relative rate / excess relative risk models
I would fit a Poisson model to the dose-response data with offsets
Von: David Winsemius [dwinsem...@comcast.net]
Gesendet: Mittwoch, 8. Januar 2014 19:06
An: Wollschlaeger, Daniel
Cc: r-help@r-project.org
Betreff: Re: [R] Linear relative rate / excess relative risk models
I would fit a Poisson model to the dose-response
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