Bayesian model selection in logistic regression for the detection of adverse drug reactions

DOI10.1002/bimj.201500098zbMath1353.62121arXiv1505.03366OpenAlexW2964288099WikidataQ39736226 ScholiaQ39736226MaRDI QIDQ2833473

Mohammed Sedki, Pascale Tubert-Bitter, Matthieu Marbac

Publication date: 18 November 2016

Published in: Biometrical Journal (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1505.03366

zbMATH Keywords

model selectionBayesian information criterionlogistic regressionMetropolis-Hastings algorithmBayesian model selectionbinary dataadverse drug reactionspharmacovigilancespontaneous reportingFrench pharmacovigilance databaseLasso type penalty

Mathematics Subject Classification ID

Related Items (1)

A family of block-wise one-factor distributions for modeling high-dimensional binary data

Uses Software

• R
• glmnet
• MHTrajectoryR
• PhViD
• GitHub

Cites Work

• Estimating the dimension of a model
• False Discovery Rate Estimation for Frequentist Pharmacovigilance Signal Detection Methods
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