# youden: Computes optimal cutoff point using the Youden index criteria In cenROC: Estimating Time-Dependent ROC Curve and AUC for Censored Data

 youden R Documentation

## Computes optimal cutoff point using the Youden index criteria

### Description

This function computes the optimal cutoff point using the Youden index criteria of both right and interval censored time-to-event data. The Youden index estimator can be either empirical (non-smoothed) or smoothed with/without boundary correction.

### Usage

``````youden(est, plot = "FALSE")
``````

### Arguments

 `est` The object returned either by `cenROC` or `IntROC`. `plot` The logical parameter to see the ROC curve plot along with the Youden inex. The default is `TRUE`.

### Details

In medical decision-making, obtaining the optimal cutoff value is crucial to identify subject at high risk of experiencing the event of interest. Therefore, it is necessary to select a marker value that classifies subjects into healthy and diseased groups. To this end, in the literature, several methods for selecting optimal cutoff point have been proposed. In this package, we only included the Youden index criteria.

### Value

Returns the following items:

`Youden.index ` The maximum Youden index value.

`cutopt ` The optimal cutoff value.

`sens ` The sensitivity corresponding to the optimal cutoff value.

`spec ` The specificity corresponding to the optimal cutoff value.

### References

Beyene, K. M. and El Ghouch A. (2022). Time-dependent ROC curve estimation for interval-censored data. Biometrical Journal, 64, 1056– 1074.

Youden, W.J. (1950). Index for rating diagnostic tests. Cancer 3, 32–35.

### Examples

``````library(cenROC)

# Right censored data
data(mayo)

resu <- cenROC(Y=mayo\$time, M=mayo\$mayoscore5, censor=mayo\$censor, t=365*6, plot="FALSE")
youden(resu,  plot="TRUE")

# Interval censored data
data(hds)

resu1 = IntROC(L=hds\$L, R=hds\$R, M=hds\$M, t=2)
youden(resu1,  plot="TRUE")
``````

cenROC documentation built on March 31, 2023, 5:19 p.m.