On Nov 20, 2009, at 9:46 AM, 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.
The general method with glm's to specify a model with fixed
coefficients is to use an offset. I believe that the coxph function
also has that facility and seem to remember that Therneau uses offsets
in some of the examples he offers in his books and technical reports.
Perhaps:
cmod <- coxph( Surv(Time,Censor)~X1, offset=4.3*X2, data= <dfname> )
Further requests about specifics should be accompanied (as suggested
by the Posting Guide) by some code that sets up a reproducible example.
--
David.
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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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
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David Winsemius, MD
Heritage Laboratories
West Hartford, CT
______________________________________________
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
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