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Van Houwelingen's Calibration Beta Survival Measure

Source: R/MeasureSurvCalibrationBeta.R
mlr_measures_surv.calib_beta.Rd

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. $$h(t|x) = h_0(t)exp(\beta \times lp)$$ where \(lp\) is the predicted linear predictor on the test data.

The model is well calibrated if the estimated \(\hat{\beta}\) coefficient (returned score) is equal to 1.

Note: Assumes fitted model is Cox PH (i.e. has an lp prediction type).

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")

Parameters

IdTypeDefaultLevels
selogicalFALSETRUE, FALSE
methodcharacterratioratio, diff

Meta Information

  • Type: "surv"

  • Range: \((-\infty, \infty)\)

  • Minimize: FALSE

  • Required prediction: lp

Parameter details

  • se (logical(1))
    If TRUE then return standard error of the measure which is the standard error of the estimated coefficient \(se_{\hat{\beta}}\) from the Cox PH model. If FALSE (default) then returns the estimated coefficient \(\hat{\beta}\).

  • method (character(1))
    Returns \(\hat{\beta}\) if equal to ratio (default) and \(|1-\hat{\beta}|\) if diff. With diff, the output score can be minimized and for example be used for tuning purposes. This parameter takes effect only if se is FALSE.

References

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

See also

Other survival measures: mlr_measures_surv.calib_alpha, 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_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, mlr_measures_surv.xu_r2

Other calibration survival measures: mlr_measures_surv.calib_alpha, mlr_measures_surv.dcalib

Other lp survival measures: mlr_measures_surv.chambless_auc, mlr_measures_surv.hung_auc, mlr_measures_surv.nagelk_r2, mlr_measures_surv.oquigley_r2, mlr_measures_surv.song_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, mlr_measures_surv.xu_r2

Super classes

mlr3::Measure -> mlr3proba::MeasureSurv -> MeasureSurvCalibrationBeta

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

MeasureSurvCalibrationBeta$new(method = "ratio")

Arguments

method

defines which output score to return, see "Parameter details" section.

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.