  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.
