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

## Details

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

The re-weighted ISS, RISS is given by $$L_{RISS}(S,t|t^*) = [(S(t^*))I(t \le t^*, \delta = 1)(1/G(t))] + [((1 - S(t^*)))I(t > t^*)(1/G(t))]$$ where $$G$$ is the Kaplan-Meier estimate of the censoring distribution, i.e. always weighted by $$G(t)$$. RISS 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 RISS. 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 task and train_set are passed to $score then $$G(t)$$ 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. If t_max or p_max is given, then $$G(t)$$ will be fitted using all observations from the train set (or test set) and only then the cutoff time will be applied. This is to ensure that more data is used for fitting the censoring distribution via the Kaplan-Meier. Setting the t_max can help alleviate inflation of the score when proper is TRUE, in cases where an observation is censored at the last observed time point. This results in $$G(t_{max}) = 0$$ and the use of eps instead (when t_max is NULL). ## Dictionary This Measure can be instantiated via the dictionary mlr_measures or with the associated sugar function msr(): MeasureSurvSchmid$new()

#### Arguments

ERV

(logical(1))
Standardize measure against a Kaplan-Meier baseline (Explained Residual Variation)

### Method clone()

The objects of this class are cloneable with this method.

#### Usage

MeasureSurvSchmid\$clone(deep = FALSE)

#### Arguments

deep

Whether to make a deep clone.