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.

npsurv(formula, data=environment(formula),
              subset, weights, na.action=na.delete, ...)

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,weights,na.action

see survfit.formula

...

see survfit.formula

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.

Details

see survfit.formula for details

See also

survfit.cph for survival curves from Cox models. print, plot, lines, coxph, strata, survplot, ggplot.npsurv

Author

Thomas Lumley tlumley@u.washington.edu and Terry Therneau

Examples

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)

ggplot(fit)   # prettier than plot()


# 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)
#> Input object size:	 35552 bytes;	 14 variables	 305 observations
#> Modified variable	stop
#> New object size:	25104 bytes;	14 variables	241 observations
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')