Prediction model for rare events in longitudinal follow-up and resampling methods

Pierre Druilhet, Mathieu Berthe, Stéphanie Léger
6/19/2023
stat.MEcs.LGstat.AP

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Abstract

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.

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Cite this paper

@article{druilhet2023prediction,
  title  = {Prediction model for rare events in longitudinal follow-up and resampling methods},
  author = {Pierre Druilhet and Mathieu Berthe and Stéphanie Léger},
  year   = {2023},
  eprint = {2306.10977},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2306.10977v1}
}

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