MHTrajectoryR-package: Detection of adverse drug events by analyzing...

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

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 <[e-mail]>

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

## 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)

MHTrajectoryR documentation built on May 1, 2019, 8:41 p.m.