MHTrajectoryR: Bayesian Model Selection in Logistic Regression for the Detection of Adverse Drug Reactions

Spontaneous adverse event reports have a high potential for detecting adverse drug reactions. However, due to their dimension, the analysis of such databases requires statistical methods. We propose to use a logistic regression whose sparsity is viewed as a model selection challenge. Since the model space is huge, a Metropolis-Hastings algorithm carries out the model selection by maximizing the BIC criterion.

AuthorMatthieu Marbac and Mohammed Sedki
Date of publication2016-04-06 13:53:39
MaintainerMohammed Sedki <Mohammed.sedki@u-psud.fr>
LicenseGPL (>=2)
Version1.0.2

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