summary.sltime: Summaries of a Super Learner

Description

Return goodness-of-fit indicators of a Super Learner obtained by the function survivalSL.

Usage

# S3 method for sltime
summary(object,  method="sl", newdata=NULL,
ROC.precision=seq(.01,.99,.01), digits=7, ...)

Value

No return value for this S3 method.

Arguments

object: An object returned by the function survivalSL.
method: A character string with the name of the algorithm included in the SL for which the calibration plot is performed. The default is "sl" for the Super Learner.
newdata: An optional data frame containing the new sample for validation with covariate values, follow-up times, and event status. The default value is NULL, the calibration plot is performed from the same subjects of the training sample.
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. 0 (min) and 1 (max) are not allowed. By default, the precision is seq(.01,.99,.01).
digits: An optional integer for the number of digits to print when printing numeric values.
...: Additional arguments affecting the summary which are passed from libsl by default. It concerns the argument times, failures, and pro.time.

Details

The following metrics are returned: "ci" for the concordance index at the prognostic time pro.time, "bs" for the Brier score at the prognostic time pro.time, "ibs" for the integrated Brier score up to the last observed time of event, "ibll" for the integrated binomial log-likelihood up to the last observed time of event, "bll" for the binomial Log-likelihood, "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 last observed time of event, "bll" for the binomial log-likelihood, and "auc" for the area under the time-dependent ROC curve up to the prognostic time pro.time.

Examples

Run this code

data(dataDIVAT2)

dataDIVAT2$train <- 1*rbinom(n=dim(dataDIVAT2)[1], size = 1, prob=1/2)

# The training of the super learner with 2 algorithms from the
   # first 100 patients of the training sample
sl1 <- survivalSL(method=c("LIB_AFTgamma", "LIB_PHgompertz"),  metric="auc",
  data=dataDIVAT2[dataDIVAT2$train==1,][1:100,],  times="times", failures="failures",
  pro.time = 12,  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"),
  cv=3)

# The prognostic capacities from the same training sample
summary(sl1)