survcomp (version 1.22.0)

td.sens.spec: Function to compute sensitivity and specificity for a binary classification of survival data

Description

The function is a wrapper for the survivalROC.C function in order to compute sensitivity and specificity for a binary classification of survival data.

Usage

td.sens.spec(cl, surv.time, surv.event, time, span = 0, sampling = FALSE, na.rm = FALSE, ...)

Arguments

cl

vector of binary classes.

surv.time

vector of times to event occurrence.

surv.event

vector of event occurrence indicators.

time

time point for sensitivity and specificity estimations.

span

Span for the NNE. Default value is 0.

sampling

jackknife procedure to estimate the standard error of sensitivity and specificity estimations.

na.rm

TRUE if the missing values should be removed from the data, FALSE otherwise.

...

additional arguments to be passed to the survivalROC function.

Value

sens

sensitivity estimate

sens.se

standard error for sensitivity estimate

spec

specificity estimate

spec.se

standard error for specificity estimate

Details

Only NNE method is used to estimate sensitivity and specificity (see survivalROC.C). The standard error for sensitivity and specificity is estimated through jackknife procedure (see jackknife).

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.

Efron, B. and Tibshirani, R. (1986). "The Bootstrap Method for standard errors, confidence intervals, and other measures of statistical accuracy", Statistical Science, 1 (1), pages 1--35.

See Also

survivalROC

Examples

set.seed(12345)
gender <- sample(c(0,1), 100, replace=TRUE)
stime <- rexp(100)
cens <- runif(100,.5,2)
sevent <- as.numeric(stime <= cens)
stime <- pmin(stime, cens)
mysenspec <- td.sens.spec(cl=gender, surv.time=stime, surv.event=sevent,
  time=1, span=0, na.rm=FALSE)