R: Concordance Statistics Survival Measure

mlr_measures_surv.cindex {mlr3proba} | R Documentation

## Concordance Statistics Survival Measure

### Description

Calculates weighted concordance statistics, which, depending on the chosen weighting method and tied times solution, are equivalent to several proposed methods.

For the Kaplan-Meier estimate of the training survival distribution, S, and the Kaplan-Meier estimate of the training censoring distribution, G:

`weight_meth`:

- `"I"` = No weighting. (Harrell)

- `"GH"` = Gonen and Heller's Concordance Index

- `"G"` = Weights concordance by G^-1.

- `"G2"` = Weights concordance by G^-2. (Uno et al.)

- `"SG"` = Weights concordance by S/G (Shemper et al.)

- `"S"` = Weights concordance by S (Peto and Peto)

The last three require training data. `"GH"` is only applicable to LearnerSurvCoxPH.

@details The implementation is slightly different from survival::concordance. Firstly this implementation is faster, and secondly the weights are computed on the training dataset whereas in survival::concordance the weights are computed on the same testing data.

### Dictionary

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

    MeasureSurvCindex$new()
    mlr_measures$get("surv.cindex")
    msr("surv.cindex")

### Meta Information

- Type: `"surv"`

- Range: [0, 1]

- Minimize: `FALSE`

- Required prediction: `crank`

### Super classes

`mlr3::Measure` -> `mlr3proba::MeasureSurv` -> `MeasureSurvCindex`

### Active bindings

`cutoff`

  `(numeric(1))` Cut-off time to evaluate concordance up to.

`weight_meth`

  `(numeric(1))` Method for weighting concordance.

`tiex`

  `(numeric(1))` Cut-off time to evaluate concordance up to.

### Methods

#### Public methods

- `MeasureSurvCindex$new()`

- `MeasureSurvCindex$clone()`

Inherited methods

- `mlr3::Measure$aggregate()`

- `mlr3::Measure$format()`

- `mlr3::Measure$help()`

- `mlr3::Measure$score()`

- `mlr3proba::MeasureSurv$print()`

#### Method `new()`

This is an abstract class that should not be constructed directly.

##### Usage

    MeasureSurvCindex$new(
      cutoff = NULL,
      weight_meth = c("I", "G", "G2", "SG", "S", "GH"),
      tiex = 0.5
    )

##### Arguments

`cutoff`

  (`numeric(1)`)
  Cut-off time to evaluate concordance up to.

`weight_meth`

  (`character(1)`)
  Method for weighting concordance. Default `"I"` is Harrell's C. See details.

`tiex`

  (`numeric(1)`)
  Weighting applied to tied rankings, default is to give them half weighting.

#### Method `clone()`

The objects of this class are cloneable with this method.

##### Usage

    MeasureSurvCindex$clone(deep = FALSE)

##### Arguments

`deep`

  Whether to make a deep clone.

### References

Peto, Richard, Peto, Julian (1972). “Asymptotically efficient rank invariant test procedures.” Journal of the Royal Statistical Society: Series A (General), 135(2), 185–198.

Harrell, E F, Califf, M R, Pryor, B D, Lee, L K, Rosati, A R (1982). “Evaluating the yield of medical tests.” Jama, 247(18), 2543–2546.

Gönen M, Heller G (2005). “Concordance probability and discriminatory power in proportional hazards regression.” Biometrika, 92(4), 965–970. doi: 10.1093/biomet/92.4.965.

Schemper, Michael, Wakounig, Samo, Heinze, Georg (2009). “The estimation of average hazard ratios by weighted Cox regression.” Statistics in Medicine, 28(19), 2473–2489. doi: 10.1002/sim.3623.

Uno H, Cai T, Pencina MJ, D'Agostino RB, Wei LJ (2011). “On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data.” Statistics in Medicine, n/a–n/a. doi: 10.1002/sim.4154.

### See Also

Other survival measures: `mlr_measures_surv.calib_alpha`, `mlr_measures_surv.calib_beta`, `mlr_measures_surv.chambless_auc`, `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.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`

[Package mlr3proba version 0.4.2 Index]