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

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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.

Author
Matthieu Marbac and Mohammed Sedki
Date of publication
2016-04-06 13:53:39
Maintainer
Mohammed Sedki <Mohammed.sedki@u-psud.fr>
License
GPL (>=2)
Version
1.0.2

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Man pages

Analyze_oneAE
Signal detection using via variable selection in logistic...
exampleAE
A simulated data
exampleDrugs
A simulated data
MHTrajectoryR-package
Detection of adverse drug events by analyzing...
OmopReference
The OMOP reference set

Files in this package

MHTrajectoryR/DESCRIPTION
MHTrajectoryR/NAMESPACE
MHTrajectoryR/R
MHTrajectoryR/R/Analyze_oneAE.R
MHTrajectoryR/R/ExhaustiveLogisticw.R
MHTrajectoryR/R/FindSignals.R
MHTrajectoryR/R/MHLogisticw.R
MHTrajectoryR/R/MatchOmop.R
MHTrajectoryR/data
MHTrajectoryR/data/OmopReference.rda
MHTrajectoryR/data/exampleAE.rda
MHTrajectoryR/data/exampleDrugs.rda
MHTrajectoryR/man
MHTrajectoryR/man/Analyze_oneAE.Rd
MHTrajectoryR/man/MHTrajectoryR-package.Rd
MHTrajectoryR/man/OmopReference.Rd
MHTrajectoryR/man/exampleAE.Rd
MHTrajectoryR/man/exampleDrugs.Rd