Hello Terri,
Thank you very much. This is the answer I needed. Could you also
tell me how I can calculate 25 and 50% quantiles in R? I can only get median
as far as I know.
Cem
Cem Girit
-----Original Message-----
From: Terry Therneau [mailto:thern...@mayo.edu]
Sent: Monday, October 24, 2011 10:21 AM
To: r-help@r-project.org
Cc: Cem Girit
Subject: Re: Survival analysis
On Sun, 2011-10-23 at 12:00 +0200, r-help-requ...@r-project.org wrote:
> The results by the survfit routine do not agree with the
> results of these formulae as obtained by SAS.
>
The next question should be "is SAS correct". The answer in this case is
no.
For survival data the mean is computed as the area under S(t), the
survival curve. This is how you deal with censoring. But becasue survival
curves often don't fall all the way to zero, one must deal with the question
of how far to the right the integral should go. The help file for
print.survfit has a short discussion of three possible options available in
R; two are pretty good, the third I consider more problematic, but it is
found in some textbooks. I would rank the approach used by SAS in fourth
position and have chosen not to implement it.
Assume a curve has its last death at time 43, but 3 others who survive to
time 59, 60 and 62 (this is the curve for your second group).
To compute the mean, SAS replaces those three subjects with 3 deaths at
time 43. So it gets a mean < 43 (surprise!), while R gives a more
sensible answer. If you had 100 subjects followed for 50 years, all still
alive but one (who died at year 2), the SAS answer would be a mean survival
of 2 years.
Terry Therneau
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