[R] competing risks survival analysis

[Bill Simpson]

I will have data in the following form:

Time    resp type       stim type
300     a               A
200     b               A
155     a               B
250     b               B
80      c               A
1000    d               B
...

c is left censored observation; d is right censored

This sort of problem is discussed in Chap 9 of Cox & Oakes Analysis of
Survival Data under the name "competing risks".

Observations are obtained from n independent individuals in the form
(t_i,r_i;s_i) where t_i is the time of the event (failure), r_i is the
response type (failure type), and s_i is the stimulus type (explanatory
variable).

I am wondering if it is possible to use survfit5 to fit parametric and
nonparametric models to data like these, and if so how to do it. I
read the documentation for survfit5 and Surv() did  not seem to allow for
the type of model I need. If I can't use survfit5, any suggestions on how
to proceed? I am pretty ignorant of survival analysis at this point.

(Maybe I can just do separate survival analysis runs for the type a and
type b responses?)

Thanks very much for any help.

Bill
PS In the end I would like to have a plot of phat_a(t) vs t: probablity of
a failure of type a as a function of time (just like Cox and Oakes fig
9.1)

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