A copula-based boosting model for time-to-event prediction with dependent censoring. (arXiv:2210.04869v1 [stat.ME])

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 …

arxiv boosting copula event prediction