tdrocc {survcomp}R Documentation

Function to compute time-dependent ROC curves

Description

The function is a wrapper for the survivalROC function in order to compute the time-dependent ROC curves.

Usage

tdrocc(x, surv.time, surv.event, surv.entry = NULL, time, cutpts = NA, na.rm = FALSE, verbose = FALSE, span = 0, lambda = 0, ...)

Arguments

x vector of risk scores
surv.time vector of times to event occurrence
surv.event vector of event occurrence indicators
surv.entry entry time for the subjects
time time point for the ROC curve
cutpts cut points for the risk score
na.rm TRUE if the missing values should be removed from the data, FALSE otherwise
verbose verbosity of the function
span Span for the NNE, need either lambda or span for NNE
lambda smoothing parameter for NNE
... additional arguments to be passed to the survivalROC function

Value

spec specificity estimates
sens sensitivity estimates
rule rule to compute the predictions at each cutoff
cuts cutoffs
time time point at which the time-dependent ROC is computed
survival overall survival at the time point
AUC Area Under the Curve (AUC) of teh time-dependent ROC curve
data survival data and risk score used to compute the time-dependent ROC curve

Author(s)

Benjamin Haibe-Kains

References

Heagerty, P. J. and Lumley, T. L. and Pepe, M. S. (2000) "Time-Dependent ROC Curves for Censored Survival Data and a Diagnostic Marker", Biometrics, 56, pages 337–344.

See Also

survivalROC

Examples

set.seed(12345)
age <- rnorm(100, 50, 10)
stime <- rexp(100)
cens <- runif(100,.5,2)
sevent <- as.numeric(stime <= cens)
stime <- pmin(stime, cens)
tdroc <- tdrocc(x=age, surv.time=stime, surv.event=sevent, time=1, na.rm=TRUE, verbose=FALSE)
##plot the time-dependent ROC curve
plot(1-tdroc$spec, tdroc$sens, type="l", xlab="1 - specificity", ylab="sensitivity")
lines(x=c(0,1), y=c(0,1), lty=3, col="red")

[Package survcomp version 1.0 Index]