[R] competing risks survival analysis
gb at stat.umu.se
Mon Oct 30 09:37:47 CET 2000
On Sun, 29 Oct 2000, Thomas Lumley wrote:
> On Thu, 26 Oct 2000, Bill Simpson wrote:
> > 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)
> The mixture of left and right censoring is a problem -- survival5 can only
> handle this for parametric models.
> I don't have a copy of Cox & Oakes, but if you just want to know the
> probability of a failure of type a before time t you can easily handle the
> competing risks issue. If someone has a failure of type b then you know
> that they don't have a failure of type a before time t, so you can set
> their failure time to a very large number (effective infinity). If it
Hold it! I think you should _right censor_ the observation at t. This
doesn't matter, of course, if you only are interested of _one_ particular
t, but usually one wants to do the estimation for a range of t values.
> Thomas Lumley
> Assistant Professor, Biostatistics
> University of Washington, Seattle
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