Description Usage Arguments Value Details See Also Examples
Returns a list containing the survival curve, confidence limits for the curve, and other information.
1 2 3 4 
object 
the result of a call to the 
times 
vector of times;
the returned matrix will contain 1 row for each time.
The vector will be sorted into increasing order;
missing values are not allowed.
If 
censored 
logical value: should the censoring times be included in the output?
This is ignored if the 
scale 
numeric value to rescale the survival time, e.g., if the input data to

extend 
logical value: if TRUE, prints information for all specified 
rmean 
Show restricted mean: see

... 
for future methods 
a list with the following components:
surv 
the estimate of survival at time t+0. 
time 
the timepoints on the curve. 
n.risk 
the number of subjects at risk at time t0
(but see the comments on weights in the 
n.event 
if the 
n.entered 
This is present only for counting process survival data.
If the 
n.exit.censored 
if the 
std.err 
the standard error of the survival value. 
conf.int 
level of confidence for the confidence intervals of survival. 
lower 
lower confidence limits for the curve. 
upper 
upper confidence limits for the curve. 
strata 
indicates stratification of curve estimation.
If 
call 
the statement used to create the 
na.action 
same as for 
table 
table of information that is returned from 
type 
type of data censoring. Passed through from the fit object. 
This routine has two uses: printing out a survival curve at specified
time points (often yearly), or extracting the values at specified time
points for further processing.
In the first case we normally want extend=FALSE
, i.e., don't print out
data past the end of the curve. If the times
option only
contains values beyond the last point in the curve then there is nothing
to print and an error message will result.
For the second usage we almost always want extend=TRUE
, so that the
results will have a predictable length.
The survfit
object itself will have a row of information at each
censoring or event time, it does not save information on each unique
entry time. For printout at two time points t1, t2, this function will
give the the number at risk at the smallest event times that are >= t1
and >= t2, respectively, the survival curve at the largest recorded times
<= t1 and <= t2, and the number of events and censorings in the interval
t1 < t <= t2.
When the routine is called with counting process data many users are
confused by counts that are too large.
For example, Surv(c(0,0, 5, 5), c(2, 3, 8, 10), c(1, 0, 1, 0))
followed by a request for the values at time 4.
The survfit
object has entries only at times 2, 3, 8, and 10;
there are 2 subjects at risk at time 8, so that is what will be printed.
survfit
, print.summary.survfit
1 2 
Call: survfit(formula = Surv(futime, fustat) ~ 1, data = ovarian)
time n.risk n.event survival std.err lower 95% CI upper 95% CI
59 26 1 0.962 0.0377 0.890 1.000
115 25 1 0.923 0.0523 0.826 1.000
156 24 1 0.885 0.0627 0.770 1.000
268 23 1 0.846 0.0708 0.718 0.997
329 22 1 0.808 0.0773 0.670 0.974
353 21 1 0.769 0.0826 0.623 0.949
365 20 1 0.731 0.0870 0.579 0.923
431 17 1 0.688 0.0919 0.529 0.894
464 15 1 0.642 0.0965 0.478 0.862
475 14 1 0.596 0.0999 0.429 0.828
563 12 1 0.546 0.1032 0.377 0.791
638 11 1 0.497 0.1051 0.328 0.752
Call: survfit(formula = Surv(futime, fustat) ~ rx, data = ovarian)
rx=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
59 13 1 0.923 0.0739 0.789 1.000
115 12 1 0.846 0.1001 0.671 1.000
156 11 1 0.769 0.1169 0.571 1.000
268 10 1 0.692 0.1280 0.482 0.995
329 9 1 0.615 0.1349 0.400 0.946
431 8 1 0.538 0.1383 0.326 0.891
638 5 1 0.431 0.1467 0.221 0.840
rx=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
353 13 1 0.923 0.0739 0.789 1.000
365 12 1 0.846 0.1001 0.671 1.000
464 9 1 0.752 0.1256 0.542 1.000
475 8 1 0.658 0.1407 0.433 1.000
563 7 1 0.564 0.1488 0.336 0.946
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