Description Usage Arguments Details Value Author(s) References See Also Examples

This function creates risksetROC from a survival data set

1 2 3 4 |

`Stime` |
For right censored data, this is the follow up time. For left truncated data, this is the ending time for the interval. |

`entry` |
For left truncated data, this is the entry time of the interval. The default is set to NULL for right censored data. |

`status` |
survival status, 1 if had an event and 0 otherwise |

`marker` |
marker |

`predict.time` |
time point of interest |

`method` |
either of "Cox", "LocalCox" and "Schoenfeld", default is "Cox" |

`span` |
bandwidth parameter that controls the size of a local
neighborhood, needed for |

`order` |
0 or 1, locally mean if 0 and local linear if 1, needed for method="Schoenfeld", default is 1 |

`window` |
either of "asymmetric" or "symmetric", default is asymmetric, needed for method="LocalCox" |

`prop` |
what proportion of the time-interval to consider when
doing a local Cox fitting at |

`plot` |
TRUE or FALSE, default is TRUE |

`type` |
default is "l", can be either of "p" for points, "l" for line, "b" for both |

`xlab` |
label for x-axis |

`ylab` |
label for y-axis |

`...` |
additional plot arguments |

This function creates and plots ROC based on incident/dynamic
definition
of Heagerty, et. al. based on a survival data and marker values. If
proportional hazard is assumed then method="Cox" can be used. In case
of non-proportional hazard, either of "LocalCox" or "Schoenfeld" can
be used. These two methods differ in how the smoothing is done. If
*plot="TRUE"* then the ROC curve is plotted with the diagonal
line. Additional plot arguments can be supplied.

Returns a list of the following items:

`eta` |
unique marker values for calculation of TP and FP |

`TP` |
True Positive values corresponding to unique marker values |

`FP` |
False Positive values corresponding to unique marker values |

`AUC` |
Area Under (ROC) Curve at time predict.time |

Paramita Saha

Heagerty, P.J., Zheng Y. (2005)
Survival Model Predictive Accuracy and ROC curves
*Biometrics*, **61**, 92 – 105

llCoxReg(), SchoenSmooth(), CoxWeights()

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
library(MASS)
data(VA)
survival.time=VA$stime
survival.status=VA$status
score <- VA$Karn
cell.type <- factor(VA$cell)
tx <- as.integer( VA$treat==1 )
age <- VA$age
survival.status[survival.time>500 ] <- 0
survival.time[survival.time>500 ] <- 500
fit0 <- coxph( Surv(survival.time,survival.status)
~ score + cell.type + tx + age, na.action=na.omit )
eta <- fit0$linear.predictor
ROC.CC30=risksetROC(Stime=survival.time, status=survival.status,
marker=eta, predict.time=30, method="Cox",
main="ROC Curve", lty=2, col="red")
``` |

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