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Song and Zhou's AUC Survival Measure

Source: R/MeasureSurvSongAUC.R
mlr_measures_surv.song_auc.Rd

Calls survAUC::AUC.sh().

Assumes Cox PH model specification.

Details

All measures implemented from survAUC should be used with care, we are aware of problems in implementation that sometimes cause fatal errors in R. In future updates some of these measures may be re-written and implemented directly in mlr3proba.

Dictionary

This Measure can be instantiated via the dictionary mlr_measures or with the associated sugar function msr():

MeasureSurvSongAUC$new()
mlr_measures$get("surv.song_auc")
msr("surv.song_auc")

Parameters

IdTypeDefaultLevels
timesuntyped-
integratedlogicalTRUETRUE, FALSE
typecharacterincidentincident, cumulative

Meta Information

  • Type: "surv"

  • Range: \([0, 1]\)

  • Minimize: FALSE

  • Required prediction: lp

Parameter details

  • times (numeric())
    If integrated == TRUE then a vector of time-points over which to integrate the score. If integrated == FALSE then a single time point at which to return the score.

  • integrated (logical(1))
    If TRUE (default), returns the integrated score (eg across time points); otherwise, not integrated (eg at a single time point).

  • type (character(1))
    A string defining the type of true positive rate (TPR): incident refers to incident TPR, cumulative refers to cumulative TPR.

References

Song, Xiao, Zhou, Xiao-Hua (2008). “A semiparametric approach for the covariate specific ROC curve with survival outcome.” Statistica Sinica, 18(3), 947–65. https://www.jstor.org/stable/24308524.

See also

Other survival measures: mlr_measures_surv.calib_alpha, mlr_measures_surv.calib_beta, mlr_measures_surv.chambless_auc, mlr_measures_surv.cindex, mlr_measures_surv.dcalib, mlr_measures_surv.graf, mlr_measures_surv.hung_auc, mlr_measures_surv.intlogloss, mlr_measures_surv.logloss, mlr_measures_surv.mae, mlr_measures_surv.mse, mlr_measures_surv.nagelk_r2, mlr_measures_surv.oquigley_r2, mlr_measures_surv.rcll, mlr_measures_surv.rmse, mlr_measures_surv.schmid, mlr_measures_surv.song_tnr, mlr_measures_surv.song_tpr, mlr_measures_surv.uno_auc, mlr_measures_surv.uno_tnr, mlr_measures_surv.uno_tpr, mlr_measures_surv.xu_r2

Other AUC survival measures: mlr_measures_surv.chambless_auc, mlr_measures_surv.hung_auc, mlr_measures_surv.song_tnr, mlr_measures_surv.song_tpr, mlr_measures_surv.uno_auc, mlr_measures_surv.uno_tnr, mlr_measures_surv.uno_tpr

Other lp survival measures: mlr_measures_surv.calib_beta, mlr_measures_surv.chambless_auc, mlr_measures_surv.hung_auc, mlr_measures_surv.nagelk_r2, mlr_measures_surv.oquigley_r2, mlr_measures_surv.song_tnr, mlr_measures_surv.song_tpr, mlr_measures_surv.uno_auc, mlr_measures_surv.uno_tnr, mlr_measures_surv.uno_tpr, mlr_measures_surv.xu_r2

Super classes

mlr3::Measure -> mlr3proba::MeasureSurv -> mlr3proba::MeasureSurvAUC -> MeasureSurvSongAUC

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

MeasureSurvSongAUC$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

MeasureSurvSongAUC$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

library(mlr3)

# Define a survival Task
task = tsk("lung")

# Create train and test set
part = partition(task)

# Train Cox learner on the train set
cox = lrn("surv.coxph")
cox$train(task, row_ids = part$train)

# Make predictions for the test set
p = cox$predict(task, row_ids = part$test)

# Integrated AUC score
p$score(msr("surv.song_auc"), task = task, train_set = part$train, learner = cox)
#> surv.song_auc 
#>      0.633294 

# AUC at specific time point
p$score(msr("surv.song_auc", times = 600), task = task, train_set = part$train, learner = cox)
#> surv.song_auc 
#>     0.6156616 

# Integrated AUC at specific time points
p$score(msr("surv.song_auc", times = c(100, 200, 300, 400, 500)), task = task, train_set = part$train, learner = cox)
#> surv.song_auc 
#>      0.631936