Abstract / Description of output
When modelling competing risks survival data, several techniques have been proposed in both the statistical
and machine learning literature. State-of-the-art methods have extended classical approaches with more
flexible assumptions that can improve predictive performance, allow high dimensional data and missing
values, among others. Despite this, modern approaches have not been widely employed in applied settings.
This article aims to aid the uptake of such methods by providing a condensed compendium of competing
risks survival methods with a unified notation and interpretation across approaches. We highlight available
software and, when possible, demonstrate their usage via reproducible R vignettes. Moreover, we discuss
two major concerns that can affect benchmark studies in this context: the choice of performance metrics
and reproducibility.
and machine learning literature. State-of-the-art methods have extended classical approaches with more
flexible assumptions that can improve predictive performance, allow high dimensional data and missing
values, among others. Despite this, modern approaches have not been widely employed in applied settings.
This article aims to aid the uptake of such methods by providing a condensed compendium of competing
risks survival methods with a unified notation and interpretation across approaches. We highlight available
software and, when possible, demonstrate their usage via reproducible R vignettes. Moreover, we discuss
two major concerns that can affect benchmark studies in this context: the choice of performance metrics
and reproducibility.
| Original language | English |
|---|---|
| Journal | Biometrical Journal |
| DOIs | |
| Publication status | Published - 13 Feb 2024 |