  Time-to-event analysis is a branch of statistics that has increased in
popularity during the last decades due to its many application fields, such as
predictive maintenance, customer churn prediction and population lifetime
estimation. In this paper, we review and compare the performance of several
prediction models for time-to-event analysis. These consist of semi-parametric
and parametric statistical models, in addition to machine learning approaches.
Our study is carried out on three datasets and evaluated in two different
scores (the integrated Brier score and concordance index). Moreover, we show
how ensemble methods, which surprisingly have not yet been much studied in
time-to-event analysis, can improve the prediction accuracy and enhance the
robustness of the prediction performance. We conclude the analysis with a
simulation experiment in which we evaluate the factors influencing the
performance ranking of the methods using both scores.
