Calculates the Integrated Graf Score, aka integrated Brier score or squared loss.
For an individual who dies at time \(t\), with predicted Survival function, \(S\), the Graf Score at time \(t^*\) is given by $$L(S,t|t^*) = [(S(t^*)^2)I(t \le t^*, \delta = 1)(1/G(t))] + [((1 - S(t^*))^2)I(t > t^*)(1/G(t^*))]$$ # nolint where \(G\) is the Kaplan-Meier estimate of the censoring distribution.
Note: If comparing the integrated graf score to other packages, e.g. pec, then
method = 2 should be used. However the results may still be very slightly different as
this package uses survfit to estimate the censoring distribution, in line with the Graf 1999
paper; whereas some other packages use prodlim with reverse = TRUE (meaning Kaplan-Meier is
not used).
If integrated == FALSE then the sample mean is taken for the single specified times, \(t^*\), and the returned
score is given by
$$L(S,t|t^*) = \frac{1}{N} \sum_{i=1}^N L(S_i,t_i|t^*)$$
where \(N\) is the number of observations, \(S_i\) is the predicted survival function for
individual \(i\) and \(t_i\) is their true survival time.
If integrated == TRUE then an approximation to integration is made by either taking the sample
mean over all \(T\) unique time-points (method == 1), or by taking a mean weighted by the difference
between time-points (method == 2). Then the sample mean is taken over all \(N\) observations.
$$L(S) = \frac{1}{NT} \sum_{i=1}^N \sum_{j=1}^T L(S_i,t_i|t^*_j)$$
This Measure can be instantiated via the dictionary mlr_measures or with the associated sugar function msr():
MeasureSurvGraf$new()
mlr_measures$get("surv.graf")
msr("surv.graf")
Type: "surv"
Range: \([0, \infty)\)
Minimize: TRUE
Required prediction: distr
mlr3::Measure -> mlr3proba::MeasureSurv -> mlr3proba::MeasureSurvIntegrated -> MeasureSurvGraf
se(logical(1))
If TRUE returns the standard error of the measure.
new()Creates a new instance of this R6 class.
MeasureSurvGraf$new(integrated = TRUE, times, method = 2, se = FALSE)
integrated(logical(1))
If TRUE (default), returns the integrated score; otherwise, not integrated.
times(numeric())
If integrate == TRUE then a vector of time-points over which to integrate the score.
If integrate == FALSE then a single time point at which to return the score.
method(integer(1))
If integrate == TRUE selects the integration weighting method.
method == 1 corresponds to weighting each time-point equally and taking the mean score over
discrete time-points. method == 2 corresponds to calculating a mean weighted by the difference
between time-points. method == 2 is default to be in line with other packages.
se(logical(1))
If TRUE returns the standard error of the measure.
clone()The objects of this class are cloneable with this method.
MeasureSurvGraf$clone(deep = FALSE)
deepWhether to make a deep clone.
Graf E, Schmoor C, Sauerbrei W, Schumacher M (1999). “Assessment and comparison of prognostic classification schemes for survival data.” Statistics in Medicine, 18(17-18), 2529--2545. 10.1002/(sici)1097-0258(19990915/30)18:17/18<2529::aid-sim274>3.0.co;2-5.
Other survival measures:
mlr_measures_surv.beggC,
mlr_measures_surv.calib_alpha,
mlr_measures_surv.calib_beta,
mlr_measures_surv.chambless_auc,
mlr_measures_surv.cindex,
mlr_measures_surv.gonenC,
mlr_measures_surv.grafSE,
mlr_measures_surv.harrellC,
mlr_measures_surv.hung_auc,
mlr_measures_surv.intloglossSE,
mlr_measures_surv.intlogloss,
mlr_measures_surv.logloss_se,
mlr_measures_surv.logloss,
mlr_measures_surv.maeSE,
mlr_measures_surv.mae,
mlr_measures_surv.mseSE,
mlr_measures_surv.mse,
mlr_measures_surv.nagelk_r2,
mlr_measures_surv.oquigley_r2,
mlr_measures_surv.rmseSE,
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.unoC,
mlr_measures_surv.uno_auc,
mlr_measures_surv.uno_tnr,
mlr_measures_surv.uno_tpr,
mlr_measures_surv.xu_r2
Other Probabilistic survival measures:
mlr_measures_surv.grafSE,
mlr_measures_surv.intloglossSE,
mlr_measures_surv.intlogloss,
mlr_measures_surv.logloss_se,
mlr_measures_surv.logloss,
mlr_measures_surv.schmid
Other distr survival measures:
mlr_measures_surv.calib_alpha,
mlr_measures_surv.grafSE,
mlr_measures_surv.intloglossSE,
mlr_measures_surv.intlogloss,
mlr_measures_surv.logloss_se,
mlr_measures_surv.logloss,
mlr_measures_surv.schmid