survivalSL (version 0.98)

metrics: Metrics to Evaluate the Prognostic Capacities

Description

Compute several metrics to evaluate the prognostic capacities with time-to-event data.

Usage

metrics(metric, formula=NULL, data=NULL,
survivals.matrix=NULL, hazards.matrix=NULL, prediction.times=NULL,
object=NULL, pro.time=NULL, ROC.precision=seq(.01, .99, by=.01))

Value

A numeric value with the metric estimation.

Arguments

metric

The metric to compute. See details.

formula

The formula used to build the survivals.matrix.

data

A data frame for in which to look for the variables related to the status of the follow-up time.

survivals.matrix

A matrix with the predictions of survivals of each subject (lines) for each prognostic times (columns).

hazards.matrix

A matrix with the predictions of hazards of each subject (lines) for each prognostic times (columns).

prediction.times

A vector of numeric values with the times of the predictions (same length than the number of columns of prediction.matrix).

pro.time

This optional value of prognostic time represents the maximum delay for which the capacity of the variable is evaluated. The same unit than the one used in the argument times. Not used for the following metrics: "ll", "ibs", and "ibll". Default value is the time at which half of the subjects are still at risk.

object

An object of type libsl, is NULL by default. When a value is assigned to it, the other parameters automatically inherit the formula, predictions, times of predictions and data from the object. When the object is set to NULL, the parameters formula, survivals.matrix, prediction.times and data must be defined.

ROC.precision

An optional argument with the percentiles (between 0 and 1) of the prognostic variable used for computing each point of the time dependent ROC curve. Only used when metric="auc". 0 (min) and 1 (max) are not allowed. By default, the precision is seq(.01,.99,.01).

Details

The following metrics can be used: "bs" for the Brier score at the prognostic time pro.time, "p_ci" and "uno_ci" for the concordance index at the prognostic time pro.time (Pencina and Uno versions), "ll" for the log-likelihood, "ibs" for the integrated Brier score up to the last observed time in the training data, "ibll" for the integrated binomial log-likelihood up to the last observed time in the training data, "ribs" for the restricted integrated Brier score up to the prognostic time pro.time, "ribll" for the restricted integrated binomial log-likelihood up to the prognostic time pro.time, "bll" for the binomial log-likelihood, "auc" for the area under the time-dependent ROC curve up to the prognostic time pro.time.

Examples

data("dataDIVAT2")


# The estimation of the model

formula<-Surv(times,failures) ~ age + hla + retransplant + ecd
model <- LIB_COXridge(formula, data=dataDIVAT2, lambda=.1)

# The apparent AUC

metrics(metric="auc", object=model)

# The integrated Brier score up to 10 years post-transplanation

metrics(metric="ribs", object=model, pro.time=10)