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.
| Version: |
1.0.1 |
| Depends: |
R (≥ 2.10) |
| Imports: |
parallel, mgcv |
| Published: |
2016-04-05 |
| Author: |
Matthieu Marbac and Mohammed Sedki |
| Maintainer: |
Mohammed Sedki <Mohammed.sedki at u-psud.fr> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
no |
| CRAN checks: |
MHTrajectoryR results |
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