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In statistics, time-to-event analysis methods traditionally focus on theestimation of hazards. In recent years, machine learning methods have beenproposed to directly predict the event times. We propose a method based on vinecopula models to make point and interval predictions for a right-censoredresponse variable given mixed discrete-continuous explanatory variables.Extensive experiments on simulated and real datasets show that our proposedvine copula approach provides a decent approximation to other time-to-eventanalysis models including Cox proportional hazards and Accelerate Failure Timemodels. When the Cox proportional hazards or Accelerate Failure Timeassumptions do not hold, predictions based on vine copulas can significantlyoutperform other models, depending on the shape of the conditional quantilefunctions. This shows the flexibility of our proposed vine copula approach forgeneral time-to-event datasets.

Predicting Times to Event Based on Vine Copula Models