MHTrajectoryR: Bayesian Model Selection in Logistic Regression for the Detection of Adverse Drug Reactions
Version 1.0.1

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.

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AuthorMatthieu Marbac and Mohammed Sedki
Date of publication2016-04-05 17:40:22
MaintainerMohammed Sedki <Mohammed.sedki@u-psud.fr>
LicenseGPL (>= 2)
Version1.0.1
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("MHTrajectoryR")

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

Functions

Analyze_oneAE Man page Source code
ExhaustiveLogisticw Source code
FindSignals Source code
MHLogisticw Source code
MHTrajectoryR Man page
MHTrajectoryR-package Man page
MatchOmop Source code
OmopReference Man page
exampleAE Man page
exampleDrugs Man page

Files

NAMESPACE
data
data/OmopReference.rda
data/exampleAE.rda
data/exampleDrugs.rda
R
R/FindSignals.R
R/ExhaustiveLogisticw.R
R/Analyze_oneAE.R
R/MHLogisticw.R
R/MatchOmop.R
MD5
DESCRIPTION
man
man/exampleDrugs.Rd
man/exampleAE.Rd
man/OmopReference.Rd
man/Analyze_oneAE.Rd
man/MHTrajectoryR-package.Rd
MHTrajectoryR documentation built on May 19, 2017, 8:29 a.m.