Description Usage Arguments Details Value References See Also Examples
Computes an estimate of a survival curve for censored data using the AalenJohansen estimator. For ordinary (single event) survival this reduces to the KaplanMeier estimate.
1 2 3 4 
formula 
a formula object, which must have a

data 
a data frame in which to interpret the variables named in the formula,

weights 
The weights must be nonnegative and it is strongly recommended that
they be strictly positive, since zero weights are ambiguous, compared
to use of the 
subset 
expression saying that only a subset of the rows of the data should be used in the fit. 
na.action 
a missingdata filter function, applied to the model frame, after any

stype 
the method to be used estimation of the survival curve: 1 = direct, 2 = exp(cumulative hazard). 
ctype 
the method to be used for estimation of the cumulative hazard: 1 = NelsonAalen formula, 2 = FlemingHarrington correction for tied events. 
id 
identifies individual subjects, when a given person can have multiple lines of data. 
cluster 
used to group observations for the infinitesmal jackknife variance estimate, defaults to the value of id. 
robust 
logical, should the function compute a robust variance. For multistate survival curves this is true by default. For single state data see details, below. 
istate 
for multistate models, identifies the initial state of each subject or observation 
timefix 
process times through the 
etype 
a variable giving the type of event. This has been superseded by multistate Surv objects and is depricated; see example below. 
error 
this argument is no longer used 
... 
The following additional arguments are passed to internal functions
called by

If there is a data
argument, then variables in the formula
,
codeweights, subset
, id
, cluster
and
istate
arguments will be searched for in that data set.
The routine returns both an estimated probability in state and an
estimated cumulative hazard estimate.
The cumulative hazard estimate is the NelsonAalen (NA) estimate or the
FlemingHarrington (FH) estimate, the latter includes a correction for
tied event times. The estimated probability in state can estimated
either using the exponential of the cumulative hazard, or as a direct
estimate using the AalenJohansen approach.
For single state data the AJ estimate reduces to the KaplanMeier and
the probability in state to the survival curve;
for competing risks data the AJ reduces to the cumulative incidence (CI)
estimator.
For backward compatability the type
argument can be used instead.
When the data set includes left censored or interval censored data (or both), then the EM approach of Turnbull is used to compute the overall curve. Currently this algorithm is very slow, only a survival curve is produced, and it does not support a robust variance.
Robust variance:
If a robust
is TRUE, or for multistate
curves, then the standard
errors of the results will be based on an infinitesimal jackknife (IJ)
estimate, otherwise the standard model based estimate will be used.
For single state curves, the default for robust
will be TRUE
if one of: there is a cluster
argument, there
are noninteger weights, or there is a id
statement
and at least one of the id values has multiple events, and FALSE otherwise.
The default represents our best guess about when one would most
often desire a robust variance.
When there are noninteger case weights and (time1, time2) survival
data the routine is at an impasse: a robust variance likely is called
for, but requires either id
or cluster
information to be
done correctly; it will default to robust=FALSE.
With the IJ estimate, the leverage values themselves can be returned
as arrays with dimensions: number of subjects, number of unique times,
and for a multistate model, the number of unique states.
Be forwarned that these arrays can be huge. If there is a
cluster
argument this first dimension will be the number of
clusters and the variance will be a grouped IJ estimate; this can be
an important tool for reducing the size.
A numeric value for the influence
argument allows finer
control: 0= return neither (same as FALSE), 1= return the influence
array for probability in state, 2= return the influence array for the
cumulative hazard, 3= both (same as TRUE).
an object of class "survfit"
.
See survfit.object
for
details. Methods defined for survfit objects are
print
, plot
,
lines
, and points
.
Dorey, F. J. and Korn, E. L. (1987). Effective sample sizes for confidence intervals for survival probabilities. Statistics in Medicine 6, 67987.
Fleming, T. H. and Harrington, D. P. (1984). Nonparametric estimation of the survival distribution in censored data. Comm. in Statistics 13, 246986.
Kalbfleisch, J. D. and Prentice, R. L. (1980). The Statistical Analysis of Failure Time Data. New York:Wiley.
Kyle, R. A. (1997). Moncolonal gammopathy of undetermined significance and solitary plasmacytoma. Implications for progression to overt multiple myeloma}, Hematology/Oncology Clinics N. Amer. 11, 7187.
Link, C. L. (1984). Confidence intervals for the survival function using Cox's proportional hazards model with covariates. Biometrics 40, 601610.
Turnbull, B. W. (1974). Nonparametric estimation of a survivorship function with doubly censored data. J Am Stat Assoc, 69, 169173.
survfit.coxph
for survival curves from Cox models,
survfit.object
for a description of the components of a
survfit object,
print.survfit
,
plot.survfit
,
lines.survfit
,
coxph
,
Surv
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51  #fit a KaplanMeier and plot it
fit < survfit(Surv(time, status) ~ x, data = aml)
plot(fit, lty = 2:3)
legend(100, .8, c("Maintained", "Nonmaintained"), lty = 2:3)
#fit a Cox proportional hazards model and plot the
#predicted survival for a 60 year old
fit < coxph(Surv(futime, fustat) ~ age, data = ovarian)
plot(survfit(fit, newdata=data.frame(age=60)),
xscale=365.25, xlab = "Years", ylab="Survival")
# Here is the data set from Turnbull
# There are no interval censored subjects, only leftcensored (status=3),
# rightcensored (status 0) and observed events (status 1)
#
# Time
# 1 2 3 4
# Type of observation
# death 12 6 2 3
# losses 3 2 0 3
# late entry 2 4 2 5
#
tdata < data.frame(time =c(1,1,1,2,2,2,3,3,3,4,4,4),
status=rep(c(1,0,2),4),
n =c(12,3,2,6,2,4,2,0,2,3,3,5))
fit < survfit(Surv(time, time, status, type='interval') ~1,
data=tdata, weight=n)
#
# Three curves for patients with monoclonal gammopathy.
# 1. KM of time to PCM, ignoring death (statistically incorrect)
# 2. Competing risk curves (also known as "cumulative incidence")
# 3. Multistate, showing Pr(in each state, at time t)
#
fitKM < survfit(Surv(stop, event=='pcm') ~1, data=mgus1,
subset=(start==0))
fitCR < survfit(Surv(stop, event) ~1,
data=mgus1, subset=(start==0))
fitMS < survfit(Surv(start, stop, event) ~ 1, id=id, data=mgus1)
## Not run:
# CR curves show the competing risks
plot(fitCR, xscale=365.25, xmax=7300, mark.time=FALSE,
col=2:3, xlab="Years post diagnosis of MGUS",
ylab="P(state)")
lines(fitKM, fun='event', xmax=7300, mark.time=FALSE,
conf.int=FALSE)
text(3652, .4, "Competing risk: death", col=3)
text(5840, .15,"Competing risk: progression", col=2)
text(5480, .30,"KM:prog")
## End(Not run)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.