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.
||R (≥ 2.10)
||Matthieu Marbac and Mohammed Sedki
||Mohammed Sedki <Mohammed.sedki at u-psud.fr>
||GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
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