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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.
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Package details |
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| Author | Matthieu Marbac and Mohammed Sedki |
| Maintainer | Mohammed Sedki <Mohammed.sedki@u-psud.fr> |
| License | GPL (>=2) |
| Version | 1.0.2 |
| Package repository | View on R-Forge |
| Installation |
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