1. survreg() does NOT fit a proportional hazards model, a mistake
repeated multiple times in your post
2. The coxph function operates on the risk scale: large values of Xbeta
= large death rates = bad
The survreg operates on the time scale: large values of xbeta =
longer liftetime = good.
3.
Hi Terry, David, and Thomas,
Thank you for all your emails and the time you to took to clarify my
misunderstanding on survival analysis. I will need a bit of time to digest all
this information and to do some more reading.
Best regards,
Ben
From: Terry Therneau
1. survreg() does NOT
I manage to achieve similar results with a Cox model as follows but I don't
really understand why we have to take the inverse of the linear prediction with
the Cox model and why we do not need to divide by the number of days in the
year
anymore?
Am I getting a similar result out of pure luck?
On Nov 25, 2010, at 7:27 AM, Ben Rhelp wrote:
I manage to achieve similar results with a Cox model as follows but
I don't
really understand why we have to take the inverse of the linear
prediction with
the Cox model
Different parameterization. You can find expanded answer(s) in the
Hi David,
Thank you for your reply. See below for more information.
From: David Winsemius
On Nov 25, 2010, at 7:27 AM, Ben Rhelp wrote:
I manage to achieve similar results with a Cox model as follows but I don't
really understand why we have to take the inverse of the linear
On Nov 25, 2010, at 10:08 AM, Ben Rhelp wrote:
Hi David,
Thank you for your reply. See below for more information.
From: David Winsemius
On Nov 25, 2010, at 7:27 AM, Ben Rhelp wrote:
I manage to achieve similar results with a Cox model as follows
but I don't
really understand why
I hit the send button on my second reply before I intended to. Since
then I have noticed that the question I thought you were asking is not
at all a good match to the Subject line of your message. There is a
type =lp in predict.coxph and that is the linear predictor, although
it is not a
Hi all,
Is there an equivalent to predict(...,type=linear) of a Proportional hazard
model for a Cox model instead?
For example, the Figure 13.12 in MASS (p384) is produced by:
(aids.ps - survreg(Surv(survtime + 0.9, status) ~ state + T.categ +
pspline(age, df=6), data = Aidsp))
zz -
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