Prediction model for rare events in longitudinal follow-up and
  resampling methods
Pierre Druilhet Mathieu Berthe Stéphanie Léger
We consider the problem of model building for rare events prediction in
longitudinal follow-up studies. In this paper, we compare several resampling
methods to improve standard regression models on a real life example. We
evaluate the effect of the sampling rate on the predictive performances of the
models. To evaluate the predictive performance of a longitudinal model, we
consider a validation technique that takes into account time and corresponds to
the actual use in real life.