On Mar 13, 2011, at 2:43 PM, Ravi Varadhan wrote:
Like David, I too thought that `offset' is the way to do this. I
was actually in the midst of testing the differences between using
`offset' and `init' when David's email came.
Here is what I could figure out so far:
1. If you want to fix only a subset of regressors, but let others
be estimated, then you must use `offset'. The `init' approach will
not work.
2. Even when all the regressors are fixed (I have to admit that I do
not see the point of this, like David said), there seems to be a
difference in using `init' and `offset'.
I want to thank you, Ravi, for taking the next steps beyond my
speculations. However, I did not mean to imply that I could see no
point in using an offset with coxph(). I only meant to say that the OP
had not yet provided a basis for doing so.
If one were trying to test a pre-determined classification rule
against a new or augmented candidate rule, then entering an offset
term could be very desirable. An example in my domain of interest
might be to use a set of life-table estimates for the effect of sex
and age , then including other covariates, and even including a
subject_age term to test whether there was a departure from the
population expectations. I admit that I have not seen worked examples
using coxph(), but Therneau has offered examples using Poisson models
with glm() in his publications regarding "expected survival" both in
Mayo Clinic Technical Reports and in his book with Grambsch, "Modeling
Survival Data".
First of all, we cannot interpret or use the standard errors, CIs,
abd p-values when iter.max=0. Secondly, there is major disagreement
in the predictions between `offset' and `init' with no iterations.
You can run the following code to verify this:
ans1 <- coxph(Surv(time, status) ~ age + ph.karno, data = lung, init
= c(0.05, -0.05), iter.max = 0)
ans2 <- coxph(Surv(time, status) ~ offset(0.05*age) +
offset(-0.05*ph.karno), data = lung)
lp1 <- predict(ans1, type="lp")
lp2 <- predict(ans2, type="lp")
all.equal(lp1, lp2)
all.equal(lp1, lp2)
[1] "Mean relative difference: 1.463598"
The results from `offset' are correct, i.e. lp2 can be readily
verified to be equal to 0.05 * (age - ph.karno). I don't know how
lp1 is computed.
Ravi.
____________________________________________________________________
Ravi Varadhan, Ph.D.
Assistant Professor,
Division of Geriatric Medicine and Gerontology
School of Medicine
Johns Hopkins University
Ph. (410) 502-2619
email: rvarad...@jhmi.edu
----- Original Message -----
From: David Winsemius <dwinsem...@comcast.net>
Date: Sunday, March 13, 2011 2:29 pm
Subject: Re: [R] using pre-calculated coefficients and LP in coxph()?
To: Dimitris Rizopoulos <d.rizopou...@erasmusmc.nl>
Cc: r-help@r-project.org, Angel Russo <angerusso1...@gmail.com>
On Mar 13, 2011, at 1:32 PM, Dimitris Rizopoulos wrote:
probably you want to use the 'init' argument and 'iter.max'
control-argument of coxph(). For example, for the Lung dataset, we
fix
the coefficients of age and ph.karno at 0.05 and -0.05, respectively:
library(survival)
coxph(Surv(time, status) ~ age + ph.karno, data = lung,
init = c(0.05, -0.05), iter.max = 0)
I hope it helps.
Best,
Dimitris
On 3/13/2011 6:08 PM, Angel Russo wrote:
I need to force a coxph() function in R to use a pre-calculated set
of beta
coefficients of a gene signature consisting of xx genes and the
gene
expression is also provided of those xx genes.
I would have guessed (and that is all one can do without an example
and better description of what the setting and goal might be) that
the
use of the offset capablity in coxph might be needed.
--
David.
If I try to use "coxph()" function in R using just the gene
expression data
alone, the beta coefficients and coxph$linear.predictors will
change and I
need to use the pre-calcuated linear predictor not re-computed
using coxph()
function. The reason is I need to compute a quantity that uses as
it's input
the coxph() output but I need this output to be pre-calculated
beta-coefficients and linear.predictor.
Any one can show me how to do this in R?
Thanks a lot.
David Winsemius, MD
West Hartford, CT
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David Winsemius, MD
West Hartford, CT
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