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||2016-04-06 13:53:39|
|Maintainer||Mohammed Sedki <Mohammed.firstname.lastname@example.org>|
|Package repository||View on R-Forge|
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