# survfit.formula

From rms v2.0-2
by Frank E Harrell Jr

##### Compute a Survival Curve for Censored Data

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).

##### Usage

```
## S3 method for class 'formula':
survfit(formula, data, \dots)
```

##### Arguments

- formula
- a formula object, which 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. - data
- a data frame in which to interpret the variables named in the formula,
`subset`

and`weights`

arguments. - ...
- see
`survfit.formula`

##### Details

see `survfit.formula`

for details

##### Value

- an object of class
`"survfit"`

. See`survfit.object`

for details. Methods defined for survfit objects are`print`

,`plot`

,`lines`

, and`points`

.

##### See Also

`survfit.cph`

for survival curves from Cox models.
`print`

,
`plot`

,
`lines`

,
`coxph`

,
`Surv`

,
`strata`

.

##### Examples

```
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")
```

*Documentation reproduced from package rms, version 2.0-2, License: GPL (>= 2)*

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