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.

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

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`

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 `survfit.formula`

for details

`survfit.cph`

for survival curves from Cox models.
`print`

,
`plot`

,
`lines`

,
`coxph`

,
`strata`

,
`survplot`

# 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') # }