[R] How do I specify a partially completed survival analysis model

[Terry Therneau]

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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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1. Use an offset argument.  Anything therein is put into the linear
predictor "as is".
      coxph(Surv(Time, Censor) ~ X1 + offset(X2*b2), data=a)

2. .... method='breslow'
 This never ceases to amaze me.  The Efron approximation is uniformly
superior to the Breslow -- that's why it is the default --- but the
inferior method remains more popular to the point that people force the
program to use it.  I suppose because it was easier to program and thus
was the first one implimented.  However, for simulated data there will
not be any ties in "time" so the two are identical.

Terry Therneau