[R] competing risks survival analysis

[gb]

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
> 
> Thomas Lumley
> Assistant Professor, Biostatistics
> University of Washington, Seattle
> 
> 
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Göran
-- 
 Göran Broström                      tel: +46 90 786 5223
 Professor                           fax: +46 90 786 6614
 Department of Statistics            http://www.stat.umu.se/egna/gb/
 Umeå University
 SE-90187 Umeå, Sweden             e-mail: gb at stat.umu.se

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