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():
$new()
MeasureSurvCindex$get("surv.cindex")
mlr_measuresmsr("surv.cindex")
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.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
Super classes
mlr3::Measure
-> mlr3proba::MeasureSurv
-> MeasureSurvCindex
Methods
Method new()
This is an abstract class that should not be constructed directly.
Usage
MeasureSurvCindex$new()