survfit {survival}  R Documentation 
Computes an estimate of a survival curve for censored data using either the KaplanMeier or the FlemingHarrington method or computes the predicted survivor function for a Cox proportional hazards model.
survfit(formula, data, weights, subset, na.action, newdata, individual=F, conf.int=.95, se.fit=T, type=c("kaplanmeier","flemingharrington", "fh2"), error=c("greenwood","tsiatis"), conf.type=c("log","loglog","plain","none"), conf.lower=c("usual", "peto", "modified")) ## S3 method for class 'survfit': x[...,drop=FALSE] basehaz(fit,centered=TRUE)
formula 
A formula object or a coxph object.
If a formula object is supplied it must have a Surv object as the
response on the left of the ~ operator and, if desired, terms
separated by + operators on the right.
One of the terms may be a strata object. For a single survival curve
the "~ 1" part of the formula is not required.

data 
a data frame in which to interpret the variables named in the formula,
or in the subset and the weights argument.

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 argument.

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
subset argument has been used.
Default is options()$na.action .

newdata 
a data frame with the same variable names as those that appear
in the coxph formula. Only applicable when formula is a coxph object.
The curve(s) produced will be representative of a cohort who's
covariates correspond to the values in newdata .
Default is the mean of the covariates used in the coxph fit.

individual 
a logical value indicating whether the data frame represents different
time epochs for only one individual (T), or whether multiple rows indicate
multiple individuals (F, the default). If the former only one curve
will be produced; if the latter there will be one curve per row in newdata .

conf.int 
the level for a twosided confidence interval on the survival curve(s). Default is 0.95. 
se.fit 
a logical value indicating whether standard errors should be
computed. Default is TRUE .

type 
a character string specifying the type of survival curve.
Possible values are "kaplanmeier" , "flemingharrington" or "fh2"
if a formula is given
and "aalen" or "kaplanmeier" if the first argument is a coxph object,
(only the first two characters are necessary).
The default is "aalen" when a coxph object is given,
and it is "kaplanmeier" otherwise.

error 
either the string "greenwood" for the Greenwood formula or
"tsiatis" for the Tsiatis formula, (only the first character is
necessary). The default is "tsiatis" when a coxph object is
given, and it is "greenwood" otherwise.

conf.type 
One of "none" , "plain" , "log" (the default), or "loglog" . Only
enough of the string to uniquely identify it is necessary.
The first option causes confidence intervals not to be
generated. The second causes the standard intervals
curve + k *se(curve) , where k is determined from
conf.int . The log option calculates intervals based on the
cumulative hazard or log(survival). The last option bases
intervals on the log hazard or log(log(survival)). These
last will never extend past 0 or 1.

conf.lower 
controls modified lower limits to the curve,
the upper limit remains unchanged. The modified lower limit
is based on an 'effective n' argument. The confidence
bands will agree with the usual calculation at each death time, but unlike
the usual bands the confidence interval becomes wider at each censored
observation. The extra width is obtained by multiplying the usual
variance by a factor m/n, where n is the number currently at risk and
m is the number at risk at the last death time. (The bands thus agree
with the unmodified bands at each death time.)
This is especially useful for survival curves with a long flat tail.
The Peto lower limit is based on the same 'effective n' argument as the modified limit, but also replaces the usual Greenwood variance term with a simple approximation. It is known to be conservative. 
x 
a survfit object 
fit 
a coxph object 
centered 
Compute the baseline hazard at the covariate mean rather than at zero? 
drop 
Only FALSE is supported 
... 
Other arguments for future expansion 
Actually, the estimates used are the KalbfleischPrentice
(Kalbfleisch and Prentice, 1980, p.86) and the Tsiatis/Link/Breslow,
which reduce to the KaplanMeier and FlemingHarrington estimates,
respectively, when the weights are unity. When curves are fit for a
Cox model, subject weights of exp(sum(coef*(xcenter)))
are used,
ignoring any value for weights
input by the user. There is also an extra
term in the variance of the curve, due to the variance ofthe coefficients and
hence variance in the computed weights.
The Greenwood formula for the variance is a sum of terms
d/(n*(nm)), where d is the number of deaths at a given time point, n
is the sum of weights
for all individuals still at risk at that time, and
m is the sum of weights
for the deaths at that time. The
justification is based on a binomial argument when weights are all
equal to one; extension to the weighted case is ad hoc. Tsiatis
(1981) proposes a sum of terms d/(n*n), based on a counting process
argument which includes the weighted case.
The two variants of the FH estimate have to do with how ties are handled.
If there were 3 deaths out of 10 at risk, then the first would increment
the hazard by 3/10 and the second by 1/10 + 1/9 + 1/8. For curves created
after a Cox model these correspond to the Breslow and Efron estimates,
respectively, and the proper choice is made automatically.
The fh2
method will give results closer to the KaplanMeier.
Based on the work of Link (1984), the log transform is expected to produce the most accurate confidence intervals. If there is heavy censoring, then based on the work of Dorey and Korn (1987) the modified estimate will give a more reliable confidence band for the tails of the curve.
a survfit
object; see the help on survfit.object
for
details. Methods defined for survfit
objects are provided for
print
, plot
, lines
, and points
.
For basehaz
, a dataframe with the baseline hazard, times, and
strata.
The "["
method returns a survfit
object giving survival
for the selected groups.
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. Wiley, New York.
Link, C. L. (1984). Confidence intervals for the survival function using Cox's proportional hazards model with covariates. Biometrics 40, 601610.
Tsiatis, A. (1981). A large sample study of the estimate for the integrated hazard function in Cox's regression model for survival data. Annals of Statistics 9, 93108.
print.survfit
, plot.survfit
,
lines.survfit
, summary.survfit
, survfit.object
coxph
, Surv
, strata
.
#fit a KaplanMeier and plot it fit < survfit(Surv(time, status) ~ x, data=aml) plot(fit) # plot only 1 of the 2 curves from above plot(fit[2]) #fit a cox proportional hazards model and plot the #predicted survival curve fit < coxph( Surv(futime,fustat)~resid.ds+rx+ecog.ps,data=ovarian) plot( survfit( fit))