This calibration method fits the predicted linear predictor from a Cox PH model as the only predictor in a new Cox PH model with the test data as the response. $$h(t|x) = h_0(t)exp(l\beta)$$ where \(l\) is the predicted linear predictor.

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

Assumes fitted model is Cox PH.

Dictionary

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

MeasureSurvCalibrationBeta$new()
mlr_measures$get("surv.calib_beta")
msr("surv.calib_beta")

Meta Information

  • Type: "surv"

  • Range: \((-\infty, \infty)\)

  • Minimize: FALSE

  • Required prediction: lp

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 -> MeasureSurvCalibrationBeta

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

MeasureSurvCalibrationBeta$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

MeasureSurvCalibrationBeta$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.