  A characteristic feature of time-to-event data analysis is possible censoring
of the event time. Most of the statistical learning methods for handling
censored data are limited by the assumption of independent censoring, even if
this can lead to biased predictions when the assumption does not hold. This
paper introduces Clayton-boost, a boosting approach built upon the accelerated
failure time model, which uses a Clayton copula to handle the dependency
between the event and censoring distributions. By taking advantage of a copula,
the independent censoring assumption is not needed any more. During comparisons
with commonly used methods, Clayton-boost shows a strong ability to remove
prediction bias at the presence of dependent censoring and outperforms the
comparing methods either if the dependency strength or percentage censoring are
considerable. The encouraging performance of Clayton-boost shows that there is
indeed reasons to be critical about the independent censoring assumption, and
that real-world data could highly benefit from modelling the potential
dependency.
