IntROC | R Documentation |

This function computes the time-dependent ROC curve for interval censored survival data using the cumulative sensitivity and dynamic specificity definitions. The ROC curves can be either empirical (non-smoothed) or smoothed with/without boundary correction. It also calculates the time-dependent AUC.

```
IntROC(L, R, M, t, U = NULL, method = "emp", method2 = "pa", dist = "weibull",
bw = NULL, ktype = "normal", len = 151, B = 0, alpha = 0.05, plot = "TRUE")
```

`L` |
The numericvector of left limit of observed time. For left censored observations |

`R` |
The numericvector of right limit of observed time. For right censored observation |

`M` |
The numeric vector of marker values. |

`t` |
A scaler time point used to calculate the ROC curve. |

`U` |
The numeric vector of cutoff values. |

`method` |
The method of ROC curve estimation. The possible options are " |

`method2` |
A character indication type of modeling. This include nonparametric |

`dist` |
A character incating the type of distribution for parametric model. This includes are |

`bw` |
A character string specifying the bandwidth estimation method. The possible options are " |

`ktype` |
A character string giving the type kernel distribution to be used for smoothing the ROC curve: " |

`len` |
The length of the grid points for ROC estimation. Default is |

`B` |
The number of bootstrap samples to be used for variance estimation. The default is |

`alpha` |
The significance level. The default is |

`plot` |
The logigal parameter to see the ROC curve plot. Default is |

This function implments time-dependent ROC curve and the corresponding AUC using the model-band and nonparametric for the estimation of conditional survival function. The empirical (non-smoothed) ROC estimate and the smoothed ROC estimate with/without boundary correction can be obtained using this function.
The smoothed ROC curve estimators require selecting a bandwidth parametr for smoothing the ROC curve. To this end, three data-driven methods: the normal reference "`NR`

", the plug-in "`PI`

" and the cross-validation "`CV`

" were implemented.
See Beyene and El Ghouch (2020) for details.

Returns the following items:

`ROC `

The vector of estimated ROC values. These will be numeric numbers between zero

and one.

`U `

The vector of grid points used.

`AUC `

A data frame of dimension `1 \times 4`

. The columns are: AUC, standard error of AUC, the lower

and upper limits of bootstrap CI.

`bw `

The computed value of bandwidth. For the empirical method this is always `NA`

.

`Dt `

The vector of estimated event status.

`M `

The vector of Marker values.

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

Beyene, K. M. and El Ghouch A. (2020). Smoothed time-dependent receiver operating characteristic curve for right censored survival data. *Statistics in Medicine*. 39: 3373– 3396.

```
library(cenROC)
data(hds)
est = IntROC(L=hds$L, R=hds$R, M=hds$M, t=2)
est$AUC
```

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