| survfit.coxph {survival} | R Documentation |
Computes the predicted survivor function for a Cox proportional hazards model.
## S3 method for class 'coxph':
survfit(formula, newdata,
se.fit=TRUE, conf.int=.95,
individual=FALSE,
type,vartype,
conf.type=c("log","log-log","plain","none"),...)
formula |
A coxph object.
|
newdata |
a data frame with the same variable names as those that appear
in the coxph formula.
The curve(s) produced will be representative of a cohort whose 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 each row of newdata represents
a distinct individual (FALSE, the default),
or if each row of the data frame represents different
time epochs for only one individual (TRUE).
In the former case the result will have one curve for each row in
newdata, in the latter only a single curve will be produced.
|
conf.int |
the level for a two-sided 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,vartype |
a character string specifying the type of survival curve.
Possible values are "aalen" or "kaplan-meier"
(only the first two characters are necessary).
The default is "aalen"
Earlier versions of survfit
used type="tsiatis" to get the "aalen" estimator. For backward
compatibility, this is still allowed.
|
conf.type |
One of "none", "plain", "log" (the default),
or "log-log". 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)).
|
... |
for future methods |
See survfit for more Details.
The estimates used are the Kalbfleisch-Prentice (Kalbfleisch and Prentice, 1980, p.86) and the Tsiatis/Link/Breslow, which reduce to the Kaplan-Meier and Fleming-Harrington estimates, respectively, when the weights are unity. When the parent data is a Cox model, there is an extra term in the variance of the curve, due to the variance of the coefficients and hence variance in the computed weights. Details of the Aalen estimator and its variance are found in Tsiatis (1981).
The two variants of the F-H estimate have to do with how ties are handled.
If there were 3 deaths out of 10 at risk, then the first
increments the hazard by 3/10 and the second
by 1/10 + 1/9 + 1/8.
For the first method S(t) = exp(H), where H is
the Nelson-Aalen cumulative hazard estimate,
whereas the fh2 method will
give results S(t) results closer to the Kaplan-Meier.
For curves created
after a Cox model these correspond to the Breslow and Efron estimates,
respectively, and the proper choice is made automatically.
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, 679-87.
Fleming, T. H. and Harrington, D. P. (1984). Nonparametric estimation of the survival distribution in censored data. Comm. in Statistics 13, 2469-86.
Kablfleisch, J. D. and Prentice, R. L. (1980). The Statistical Analysis of Failure Time Data. New York:Wiley.
Link, C. L. (1984). Confidence intervals for the survival function using Cox's proportional hazards model with covariates. Biometrics 40, 601-610.
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, 93-108.
print,
plot,
lines,
coxph,
Surv,
strata.
#fit a Kaplan-Meier 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 left-censored (status=3),
# right-censored (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)
#
# Time to progression/death for patients with monoclonal gammopathy
# Competing risk curves (cumulative incidence)
fit1 <- survfit(Surv(stop, event=='progression') ~1, data=mgus1,
subset=(start==0))
fit2 <- survfit(Surv(stop, status) ~1, data=mgus1,
subset=(start==0), etype=event) #competing risks
# CI curves are always plotted from 0 upwards, rather than 1 down
plot(fit2, fun='event', xscale=365.25, xmax=7300, mark.time=FALSE,
col=2:3, xlab="Years post diagnosis of MGUS")
lines(fit1, fun='event', xscale=365.25, xmax=7300, mark.time=FALSE,
conf.int=FALSE)
text(10, .4, "Competing Risk: death", col=3)
text(16, .15,"Competing Risk: progression", col=2)
text(15, .30,"KM:prog")