Nonparametric Survival Estimates for Censored Data
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.
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
Returns
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
require(survival)# fit a Kaplan-Meier and plot itfit <- 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 censoringm <- 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 downplot(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 riskssurvplot(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')