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 1.40.0 Index]