td.sens.spec {survcomp}R Documentation

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.

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).

Value

sens sensitivity estimate
sens.se standard error for sensitivity estimate
spec specificity estimate
spec.se standard error for specificity estimate

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.

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)

[Package survcomp version 1.1.3 Index]