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New adaptive lasso approaches for variable selection in automated pharmacovigilance signal detection

Abstract : Adverse effects of drugs are often identified after market introduction. Post-marketing pharmacovigilance aims to detect them as early as possible and relies on spontaneous reporting systems collecting suspicious cases. Signal detection tools have been developed to mine these large databases and counts of reports are analysed with disproportionality methods. To address disproportionality method biases, recent methods apply to individual observations taking into account all exposures for the same patient. In particular, the logistic lasso provides an efficient variable selection framework, yet the choice of the regularization parameter is a challenging issue and the lasso variable selection may give inconsistent results.
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https://www.hal.inserm.fr/inserm-03477446
Contributor : Odile Malbec Connect in order to contact the contributor
Submitted on : Monday, December 13, 2021 - 2:22:16 PM
Last modification on : Tuesday, January 25, 2022 - 9:16:59 AM

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Distributed under a Creative Commons Attribution 4.0 International License

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Émeline Courtois, Pascale Tubert-Bitter, Ismaïl Ahmed. New adaptive lasso approaches for variable selection in automated pharmacovigilance signal detection. BMC Medical Research Methodology, BioMed Central, 2021, 21 (1), pp.271. ⟨10.1186/s12874-021-01450-3⟩. ⟨inserm-03477446⟩

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