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

arXiv: https://arxiv.org/abs/2306.10977 · HTML: https://mlsanity.com/paper/2306.10977/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 27 references that are in the map.
- First posted on arXiv 19 Jun 2023.

## Builds on (most cited first, 1 of 1)

- [Predictive modelling of training loads and injury in Australian football](https://mlsanity.com/paper/1706.04336/) (2017)

## Abstract (arXiv)

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.