On Nov 20, 2009, at 11:07 AM, RWilliam wrote:
In reply to suggestion by David W., setting an offset parameter
doesn't seem
to work as R is not recognizing the "X2" part of coxph(
Surv(Time,Censor)~X1, offset=log(4.3*X2), data= a ). Also, here's some
sample data:
The problem, arising as a result of not having a dataset against which
to test my memories of syntactic niceties, is that glm and coxph use
different methods of supplying offsets. Thereau and Gramsch's book has
examples, but if you did not have the book you still had alternatives.
A bit of searching with the terms: coxph Therneau offset; produced
lots of hits for the occurrence of offset in warning messages so
adding -warning to that search then produced a hit to the Google books
look at T&G's text with a worked example:
> a$logX2 <- log(a$X2)
> coxph(Surv(Time,Censor)~X1 + offset(logX2), data= a )
Call:
coxph(formula = Surv(Time, Censor) ~ X1 + offset(logX2), data = a)
coef exp(coef) se(coef) z p
X1 -0.885 0.413 1.43 -0.62 0.54
#Or just:
> coxph(Surv(Time,Censor)~X1 + offset(log(4.3*X2)), data= a )
X1 X2 Time Censor
1 1 0.40619454 77.00666 0
2 1 0.20717868 100.00000 0
3 1 0.77360963 79.03463 1
4 1 0.62221954 100.00000 0
5 1 0.32191280 100.00000 0
6 1 0.73790704 72.84842 0
7 1 0.65012237 100.00000 0
8 1 0.71596105 100.00000 0
9 1 0.74787202 84.00172 0
10 1 0.66803790 41.65760 0
11 1 0.79922364 92.41999 0
12 1 0.76433736 90.99983 0
13 1 0.57014524 100.00000 0
14 1 0.39642235 100.00000 0
15 1 0.55756045 100.00000 0
16 0 0.60079340 100.00000 0
17 0 0.43630695 100.00000 0
18 0 0.09388013 100.00000 0
19 0 0.55956791 100.00000 0
20 0 0.52491597 97.71884 1
where we set the coefficient of X2 to be 8.
RWilliam wrote:
Sorry for being impatient but is there really no way of doing this
at all?
It's quite urgent so any help is very much appreciated. Thank you.
RWilliam wrote:
Hello,
I just started using R to do epidemiologic simulation research
using the
Cox proportional hazard model. I have 2 covariates X1 and X2 which
I want
to model as h(t,X)=h0(t)*exp(b1*X1+b2*X2). I assume independence
of X
from t.
After I simulate Time and Censor data vectors denoting the
censoring time
and status respectively, I can call the following function to fit
the
data into the Cox model (a is a data.frame containing 4 columns
X1, X2,
Time and Censor):
b = coxph (Surv (Time, Censor) ~ X1 + X2, data = a, method =
"breslow");
Now the purpose of me doing simulation is that I have another
mechanism
to generate the number b2. From the given b2 (say it's 4.3), Cox
model
can be fit to generate b1 and check how feasible the new model is.
Thus,
my question is, how do I specify such a model that is partially
completed
(as in b2 is known). I tried things like
Surv(Time,Censor)~X1+4.3*X2, but
it's not working. Thanks very much.
--
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Heritage Laboratories
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