This calibration method is defined by estimating $$\alpha = \sum \delta_i / \sum H_i(t_i)$$ where \(\delta\) is the observed censoring indicator from the test data, \(H_i\) is the predicted cumulative hazard, and \(t_i\) is the observed survival time.

The standard error is given by $$exp(1/\sqrt{\sum \delta_i})$$

The model is well calibrated if the estimated \(\alpha\) coefficient is equal to 1.

Dictionary

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

MeasureSurvCalibrationAlpha$new()
mlr_measures$get("surv.calib_alpha")
msr("surv.calib_alpha")

Meta Information

  • Type: "surv"

  • Range: \((-\infty, \infty)\)

  • Minimize: FALSE

  • Required prediction: distr

References

Van Houwelingen HC (2000). “Validation, calibration, revision and combination of prognostic survival models.” Statistics in Medicine, 19(24), 3401--3415. ISSN 02776715, doi: 10.1002/1097-0258(20001230)19:24<3401::AID-SIM554>3.0.CO;2-2 .

See also

Super classes

mlr3::Measure -> mlr3proba::MeasureSurv -> MeasureSurvCalibrationAlpha

Active bindings

se

(logical(1))
If TRUE returns the standard error of the measure.

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

MeasureSurvCalibrationAlpha$new(se = FALSE)

Arguments

se

(logical(1))
If TRUE returns the standard error of the measure.


Method clone()

The objects of this class are cloneable with this method.

Usage

MeasureSurvCalibrationAlpha$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.