This release of scikit-survival 0.8 adds some nice enhancements for validating survival models. Previously, scikit-survival only supported Harrell’s concordance index to assess the performance of survival models. While it is easy to interpret and compute, it has some shortcomings:

- it has been shown that it is too optimistic with increasing amount of censoring
^{1}, - it is not a useful measure of performance if a specific time point is of primary interest (e.g. predicting 2 year survival).

The first issue is addressed by the new concordance_index_ipcw function, which implements an alternative estimator of the concordance index.^{1,2}
The second point can be addressed by extending the well known receiver operating characteristic curve (ROC curve) to possibly censored survival times. Given a time point *t*, we can estimate how well a predictive model can distinguishing subjects who will experience an event by time *t* (sensitivity) from those who will not (specificity). The newly added function cumulative_dynamic_auc implements an estimator of the cumulative/dynamic area under the ROC for a given list of time points^{3}.
Both estimators rely on *inverse probability of censoring weighting*, which means they require access to training data to estimate the censoring distribution from. Therefore, if the amount of censoring is high, some care must be taken in selecting a suitable time range for evaluation.
For a complete list of changes see the release notes.

## Download

Pre-built conda packages are available for Linux, OSX and Windows:

```
conda install -c sebp scikit-survival
```

Alternatively, scikit-survival can be installed from source via pip:

```
pip install -U scikit-survival
```

## References

- Uno, H., Cai, T., Pencina, M. J., D’Agostino, R. B., & Wei, L. J. (2011). On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data. Statistics in Medicine, 30(10), 1105–1117.
- H. Hung and C. T. Chiang, Estimation methods for time-dependent AUC models with survival data, Canadian Journal of Statistics, vol. 38, no. 1, pp. 8–26, 2010.
- H. Uno, T. Cai, L. Tian, and L. J. Wei, Evaluating prediction rules for t-year survivors with censored regression models, Journal of the American Statistical Association, vol. 102, pp. 527–537, 2007.