Detection of adverse drug events by analyzing Metropolis-Hastings Markov chain trajectory.
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. The MHTrajectoryR package 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 through Markov chain trajectory.
|License:||GPL (>= 2)|
The main function is Analyze_oneAE.
Matthieu Marbac and Mohammed Sedki Maintainer: Mohammed Sedki <firstname.lastname@example.org>
Matthieu Marbac, Pascale Tubert-Bitter, Mohammed Sedki: Bayesian model selection in logistic regression for the detection of adverse drug reactions. (http://arxiv.org/abs/1505.03366) (accepted for publication in Biometrical Journal).
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