Penalized logistic regression with low prevalence exposures beyond high dimensional settings

Abstract : Estimating and selecting risk factors with extremely low prevalences of exposure for a binary outcome is a challenge because classical standard techniques, markedly logistic regression, often fail to provide meaningful results in such settings. While penalized regression methods are widely used in high-dimensional settings, we were able to show their usefulness in low-dimensional settings as well. Specifically, we demonstrate that Firth correction, ridge, the lasso and boosting all improve the estimation for low-prevalence risk factors. While the methods themselves are well-established, comparison studies are needed to assess their potential benefits in this context. This is done here using the dataset of a large unmatched case-control study from France (2005-2008) about the relationship between prescription medicines and road traffic accidents and an accompanying simulation study. Results show that the estimation of risk factors with prevalences below 0.1% can be drastically improved by using Firth correction and boosting in particular, especially for ultra-low prevalences. When a moderate number of low prevalence exposures is available, we recommend the use of penalized techniques.
Complete list of metadatas

https://hal.inria.fr/hal-02140472
Contributor : Marta Avalos <>
Submitted on : Monday, May 27, 2019 - 12:36:48 PM
Last modification on : Thursday, July 11, 2019 - 2:10:08 PM

Links full text

Identifiers

Collections

Citation

Sam Doerken, Marta Avalos, Emmanuel Lagarde, Martin Schumacher. Penalized logistic regression with low prevalence exposures beyond high dimensional settings. PLoS ONE, Public Library of Science, 2019, 14 (5), pp.e0217057. ⟨10.1371/journal.pone.0217057⟩. ⟨hal-02140472⟩

Share

Metrics

Record views

74