DOERKEN, S.; AVALOS FERNANDEZ, Marta; LAGARDE, Emmanuel; SCHUMACHER, M.

doi:10.1371/journal.pone.0217057

Bordeaux population health [BPH]

LAGARDE, Emmanuel

Bordeaux population health [BPH]

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PLoS ONE. 2019-05-21, vol. 14, n° 5, p. e0217057

Résumé en anglais

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.< Réduire

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https://oskar-bordeaux.fr/handle/20.500.12278/7769

DOI

http://dx.doi.org/10.1371/journal.pone.0217057

Unités de recherche

Bordeaux Population Health Research Center (BPH) - UMR 1219

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Penalized logistic regression with low prevalence exposures beyond high dimensional settings

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Résumé en anglais

Mots clés en anglais

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