# npsurv

##### Nonparametric Survival Estimates 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. Attributes of the event time variable are saved (label and
units of measurement).

For competing risks the second argument for `Surv`

should be the
event state variable, and it should be a factor variable with the first
factor level denoting right-censored observations.

##### Usage

`npsurv(formula, data, subset, na.action, …)`

##### 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. For a single survival curve the right hand side should be`~ 1`

.- data,subset,na.action
see

`survfit.formula`

- …
see

`survfit.formula`

##### Details

see `survfit.formula`

for details

##### Value

an object of class `"npsurv"`

and `"survfit"`

.
See `survfit.object`

for details. Methods defined for `survfit`

objects are `print`

, `summary`

, `plot`

,`lines`

, and
`points`

.

##### See Also

`survfit.cph`

for survival curves from Cox models.
`print`

,
`plot`

,
`lines`

,
`coxph`

,
`strata`

,
`survplot`

##### Examples

```
# NOT RUN {
require(survival)
# fit a Kaplan-Meier and plot it
fit <- npsurv(Surv(time, status) ~ x, data = aml)
plot(fit, lty = 2:3)
legend(100, .8, c("Maintained", "Nonmaintained"), lty = 2:3)
# 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 <- npsurv(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)
# status variable must be a factor with first level denoting right censoring
m <- upData(mgus1, stop = stop / 365.25, units=c(stop='years'),
labels=c(stop='Follow-up Time'), subset=start == 0)
f <- npsurv(Surv(stop, event) ~ 1, data=m)
# CI curves are always plotted from 0 upwards, rather than 1 down
plot(f, fun='event', xmax=20, mark.time=FALSE,
col=2:3, xlab="Years post diagnosis of MGUS")
text(10, .4, "Competing Risk: death", col=3)
text(16, .15,"Competing Risk: progression", col=2)
# Use survplot for enhanced displays of cumulative incidence curves for
# competing risks
survplot(f, state='pcm', n.risk=TRUE, xlim=c(0, 20), ylim=c(0, .5), col=2)
survplot(f, state='death', add=TRUE, col=3)
f <- npsurv(Surv(stop, event) ~ sex, data=m)
survplot(f, state='death', n.risk=TRUE, conf='diffbands')
# }
```

*Documentation reproduced from package rms, version 5.1-4, License: GPL (>= 2)*