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.
The re-weighted IGS, IGS* 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, i.e. always
weighted by \(G(t)\). IGS* is strictly proper when the censoring distribution is independent
of the survival distribution and when G is fit on a sufficiently large dataset. IGS is never
proper. Use proper = FALSE for IGS and proper = TRUE for IGS*, in the future the default
will be changed to proper = TRUE. Results may be very different if many observations are
censored at the last observed time due to division by 1/eps in proper = TRUE.
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 -> MeasureSurvGraf
new()Creates a new instance of this R6 class.
MeasureSurvGraf$new()
clone()The objects of this class are cloneable with this method.
MeasureSurvGraf$clone(deep = FALSE)
deepWhether to make a deep clone.
If task and train_set are passed to $score then G is fit on training data,
otherwise testing data. The first is likely to reduce any bias caused by calculating
parts of the measure on the test data it is evaluating. The training data is automatically
used in scoring resamplings.
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.calib_alpha,
mlr_measures_surv.calib_beta,
mlr_measures_surv.chambless_auc,
mlr_measures_surv.cindex,
mlr_measures_surv.dcalib,
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 Probabilistic survival measures:
mlr_measures_surv.intlogloss,
mlr_measures_surv.logloss,
mlr_measures_surv.rcll,
mlr_measures_surv.schmid
Other distr survival measures:
mlr_measures_surv.calib_alpha,
mlr_measures_surv.dcalib,
mlr_measures_surv.intlogloss,
mlr_measures_surv.logloss,
mlr_measures_surv.rcll,
mlr_measures_surv.schmid