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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.
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Submitted on : Monday, May 27, 2019 - 12:36:48 PM
Last modification on : Wednesday, April 14, 2021 - 12:26:03 PM

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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⟩



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