Returns a list containing the survival curve, confidence limits for the curve, and other information.
# S3 method for survfit
summary(object, times, censored=FALSE, scale=1,
extend=FALSE, rmean=getOption('survfit.rmean'), ...)the result of a call to the survfit function.
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=T, the default times vector contains all
the unique times in fit, otherwise
the default times vector uses only the event (death) times.
logical value: should the censoring times be included in the output?
This is ignored if the times argument is present.
numeric value to rescale the survival time, e.g., if the input data to
survfit were in
days, scale = 365.25 would scale the output to years.
logical value: if TRUE, prints information for all specified times,
even if there are no subjects left at the end of the specified
times.
This is only used if the times argument is present.
Show restricted mean: see
print.survfit for details
for future methods
a list with the following components:
the estimate of survival at time t+0.
the timepoints on the curve.
the number of subjects at risk at time t-0
(but see the comments on weights in the survfit help file).
if the times argument is missing, then this column is the number of
events that occurred at time t.
Otherwise, it is the cumulative number of events that have occurred
since the last time listed until time t+0.
This is present only for counting process survival data.
If the times argument is
missing, this column is the number of subjects that entered at time t.
Otherwise, it is the cumulative number of subjects that have entered
since the last time listed until time t.
if the times argument is
missing, this column is the number of subjects that left without an
event at time t.
Otherwise, it is the cumulative number of subjects that have left
without an event
since the last time listed until time t+0.
This is only present for counting process survival data.
the standard error of the survival value.
level of confidence for the confidence intervals of survival.
lower confidence limits for the curve.
upper confidence limits for the curve.
indicates stratification of curve estimation.
If strata is not NULL,
there are multiple curves in the result and the surv, time, n.risk, etc.
vectors will contain multiple curves, pasted end to end.
The levels of strata (a factor) are the labels for the curves.
the statement used to create the fit object.
same as for fit, if present.
table of information that is returned from print.survfit function.
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.
# NOT RUN {
summary( survfit( Surv(futime, fustat)~1, data=ovarian))
summary( survfit( Surv(futime, fustat)~rx, data=ovarian))
# }
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