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'), data.frame=FALSE, dosum, ...)
# S3 method for survfitms
summary(object, times, censored=FALSE, scale=1,
extend=FALSE, rmean=getOption('survfit.rmean'), data.frame=FALSE, ...)if data.frame = TRUE, a data frame with columns of time,
n.risk, n.event, n.censor, surv, cumhaz, strata (if present) and
data (the row of newdata for survfit.coxph). Also std.err, std.chaz,
upper and lower if the curve had se.fit=TRUE.
if data.frame = FALSE, 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.
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
if TRUE, return the results as a data frame, rather than a summary.survfit object
only applicable if times is present, see details below
for future methods
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 often want extend=TRUE, so that the
results will have a predictable length.
If data.frame = TRUE then either might be desired.
Be aware, however, that these extended values will often be badly
biased; we are essentialy treating the final censored subjects as
immortal.
The underlying survival object will have a row for each unique event or
censoring time.
When the times argument contains values not in the data, the
routine can only use a best guess for the number at risk, i.e., the
number at risk at the next event/censoring time.
When the routine is called with counting process data many users are
confused by counts that appear too large.
For example, Surv(c(0,0, 1, 5), c(2, 3, 8, 10), c(1, 0, 1, 0)),
which prints as (0,2] (0, 3+] (1, 8] (5,10+].
Do survfit followed by summary with
a request for the values at time 0.
The survfit object has entries only at times 2, 3, 8, and 10;
there are 3 subjects at risk at time 2, so that is what will be printed
for time 0.
For a printout at fixed times, for example yearly values for a curve,
the printed number of events will by default be the total number of
events that have occured since the prior line of printout, and likewise
for number of censored and number at entry, dosum = TRUE.
Alternately, the routine can return the number of events/censors/entry at
that time, dosum=FALSE.
This feature was added at the request of a user who essentially wanted to
use the times argument as a subscript to pick off selected rows
of the output, e.g., to select survival values corresponding to the
last follow-up times of a new set of observations.
The default for dosum is TRUE if the times vector is
strictly increasing and FALSE otherwise.
For a survfitms object replace the surv component with
pstate. Also, a data frame will not include the cumulative
hazard since it has a different multiplicity: one column per transition
rather than one per state.
survfit, print.summary.survfit
summary( survfit( Surv(futime, fustat)~1, data=ovarian))
summary( survfit( Surv(futime, fustat)~rx, data=ovarian))
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