tdrocc: Function to compute time-dependent ROC curves
In bhklab/survcomp: Performance Assessment and Comparison for Survival Analysis

Description Usage Arguments Value Author(s) References See Also Examples

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

1 2	tdrocc(x, surv.time, surv.event, surv.entry = NULL, time, cutpts = NA, na.rm = FALSE, verbose = FALSE, span = 0, lambda = 0, ...)

`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.

`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

Benjamin Haibe-Kains

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.

survivalROC

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(x=1-tdroc$spec, y=tdroc$sens, type="l", xlab="1 - specificity",
  ylab="sensitivity", xlim=c(0, 1), ylim=c(0, 1))
lines(x=c(0,1), y=c(0,1), lty=3, col="red")