survfit.formula {rms} | R Documentation |
Computes an estimate of a survival curve for censored data
using either the Kaplan-Meier or the Fleming-Harrington method
or computes the predicted survivor function.
For competing risks data it computes the cumulative incidence curve.
This calls the survival
package's survfit.formula
function
with a different default value for conf.type
(log-log basis). In
addition, attributes of the event time variable are saved (label and
units of measurement).
## S3 method for class 'formula' survfit(formula, data, ...)
formula |
a formula object, which must have a
|
data |
a data frame in which to interpret the variables named in the formula,
|
... |
see |
see survfit.formula
for details
an object of class "survfit"
.
See survfit.object
for
details. Methods defined for survfit objects are
print
, plot
,
lines
, and points
.
Thomas Lumley tlumley@u.washington.edu and Terry Therneau
survfit.cph
for survival curves from Cox models.
print
,
plot
,
lines
,
coxph
,
Surv
,
strata
.
require(survival) #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, weights=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")