Detection of adverse drug events by analyzing Metropolis-Hastings Markov chain trajectory.

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Description

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.

Details

Package: MHTrajectoryR
Type: Package
Version: 1.0
Date: 2016-02-07
License: GPL (>= 2)

The main function is Analyze_oneAE.

Author(s)

Matthieu Marbac and Mohammed Sedki Maintainer: Mohammed Sedki <mohammed.sedki@u-psud.fr>

References

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).

Examples

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## Not run: 
  data(exampleAE)
  data(exampleDrugs)
  res <- Analyze_oneAE(exampleAE[,1], exampleDrugs, 10, 1, 10)
  # print signals (drugs relied to the adverse event)
  print(res$signal)

## End(Not run)