Calculates the Integrated Schmid Score (ISS), aka integrated absolute loss.

For an individual who dies at time \(t\), with predicted Survival function, \(S\), the Schmid Score at time \(t^*\) is given by $$L(S,t|t^*) = [(S(t^*))I(t \le t^*, \delta = 1)(1/G(t))] + [((1 - S(t^*)))I(t > t^*)(1/G(t^*))]$$ # nolint where \(G\) is the Kaplan-Meier estimate of the censoring distribution.

The re-weighted ISS, ISS* is given by
$$L(S,t|t^*) = [(S(t^*))I(t \le t^*, \delta = 1)(1/G(t))] + [((1 - S(t^*)))I(t > t^*)(1/G(t))]$$ # nolint
where \(G\) is the Kaplan-Meier estimate of the censoring distribution, i.e. always
weighted by \(G(t)\). ISS* is strictly proper when the censoring distribution is independent
of the survival distribution and when G is fit on a sufficiently large dataset. ISS is never
proper. Use `proper = FALSE`

for ISS and `proper = TRUE`

for ISS*, 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`

.

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.
$$L(S) = \frac{1}{NT} \sum_{i=1}^N \sum_{j=1}^T L(S_i,t_i|t^*_j)$$

## Details

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.

## Dictionary

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

```
$new()
MeasureSurvSchmid$get("surv.schmid")
mlr_measuresmsr("surv.schmid")
```

## References

Schemper, Michael, Henderson, Robin (2000).
“Predictive Accuracy and Explained Variation in Cox Regression.”
*Biometrics*, **56**, 249--255.
doi:10.1002/sim.1486
.

Schmid, Matthias, Hielscher, Thomas, Augustin, Thomas, Gefeller, Olaf (2011).
“A Robust Alternative to the Schemper-Henderson Estimator of Prediction Error.”
*Biometrics*, **67**(2), 524--535.
doi:10.1111/j.1541-0420.2010.01459.x
.

## See also

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.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.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.graf`

,
`mlr_measures_surv.intlogloss`

,
`mlr_measures_surv.logloss`

,
`mlr_measures_surv.rcll`

Other distr survival measures:
`mlr_measures_surv.calib_alpha`

,
`mlr_measures_surv.dcalib`

,
`mlr_measures_surv.graf`

,
`mlr_measures_surv.intlogloss`

,
`mlr_measures_surv.logloss`

,
`mlr_measures_surv.rcll`

## Super classes

`mlr3::Measure`

-> `mlr3proba::MeasureSurv`

-> `MeasureSurvSchmid`