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||2016-04-05 17:40:22|
|Maintainer||Mohammed Sedki <Mohammed.email@example.com>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
|Installation||Install the latest version of this package by entering the following in R:
|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|
|Analyze_oneAE||Man page Source code|
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