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

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.

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

View on CRAN

Files in this package

MHTrajectoryR
MHTrajectoryR/NAMESPACE
MHTrajectoryR/data
MHTrajectoryR/data/OmopReference.rda
MHTrajectoryR/data/exampleAE.rda
MHTrajectoryR/data/exampleDrugs.rda
MHTrajectoryR/R
MHTrajectoryR/R/FindSignals.R MHTrajectoryR/R/ExhaustiveLogisticw.R MHTrajectoryR/R/Analyze_oneAE.R MHTrajectoryR/R/MHLogisticw.R MHTrajectoryR/R/MatchOmop.R
MHTrajectoryR/MD5
MHTrajectoryR/DESCRIPTION
MHTrajectoryR/man
MHTrajectoryR/man/exampleDrugs.Rd MHTrajectoryR/man/exampleAE.Rd MHTrajectoryR/man/OmopReference.Rd MHTrajectoryR/man/Analyze_oneAE.Rd MHTrajectoryR/man/MHTrajectoryR-package.Rd

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

All documentation is copyright its authors; we didn't write any of that.