Description Usage Arguments References Examples

View source: R/05-hdnom-external-validate.R

Externally Validate High-Dimensional Cox Models with Time-Dependent AUC

1 2 | ```
hdnom.external.validate(object, x, time, event, x_new, time_new, event_new,
tauc.type = c("CD", "SZ", "UNO"), tauc.time)
``` |

`object` |
Model object fitted by |

`x` |
Matrix of training data used for fitting the model. |

`time` |
Survival time of the training data.
Must be of the same length with the number of rows as |

`event` |
Status indicator of the training data,
normally 0 = alive, 1 = dead.
Must be of the same length with the number of rows as |

`x_new` |
Matrix of predictors for the external validation data. |

`time_new` |
Survival time of the external validation data.
Must be of the same length with the number of rows as |

`event_new` |
Status indicator of the external validation data,
normally 0 = alive, 1 = dead.
Must be of the same length with the number of rows as |

`tauc.type` |
Type of time-dependent AUC.
Including |

`tauc.time` |
Numeric vector. Time points at which to evaluate the time-dependent AUC. |

Chambless, L. E. and G. Diao (2006).
Estimation of time-dependent area under the ROC curve for long-term
risk prediction.
*Statistics in Medicine* 25, 3474–3486.

Song, X. and X.-H. Zhou (2008).
A semiparametric approach for the covariate specific ROC curve with
survival outcome.
*Statistica Sinica* 18, 947–965.

Uno, H., T. Cai, L. Tian, and L. J. Wei (2007).
Evaluating prediction rules for t-year survivors with censored
regression models.
*Journal of the American Statistical Association* 102, 527–537.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | ```
library("survival")
# Load imputed SMART data
data(smart)
# Use the first 1000 samples as training data
# (the data used for internal validation)
x = as.matrix(smart[, -c(1, 2)])[1:1000, ]
time = smart$TEVENT[1:1000]
event = smart$EVENT[1:1000]
# Take the next 1000 samples as external validation data
# In practice, usually use data collected in other studies
x_new = as.matrix(smart[, -c(1, 2)])[1001:2000, ]
time_new = smart$TEVENT[1001:2000]
event_new = smart$EVENT[1001:2000]
# Fit Cox model with lasso penalty
fit = hdcox.lasso(
x, Surv(time, event),
nfolds = 5, rule = "lambda.1se", seed = 11)
# External validation with time-dependent AUC
val.ext = hdnom.external.validate(
fit, x, time, event,
x_new, time_new, event_new,
tauc.type = "UNO",
tauc.time = seq(0.25, 2, 0.25) * 365)
print(val.ext)
summary(val.ext)
plot(val.ext)
``` |

road2stat/hdnom documentation built on Nov. 13, 2018, 9:06 a.m.

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.