  Motivation: Spontaneous adverse event reports have a high potential for
detecting adverse drug reactions. However, due to their dimension, exploring
such databases requires statistical methods. In this context,
disproportionality measures are used. However, by projecting the data onto
contingency tables, these methods become sensitive to the problem of
co-prescriptions and masking effects. Recently, logistic regressions have been
used with a Lasso type penalty to perform the detection of associations between
drugs and adverse events. However, the choice of the penalty value is open to
criticism while it strongly influences the results. Results: In this paper, 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.
Thus, we avoid the calibration of penalty or threshold. During our application
on the French pharmacovigilance database, the proposed method is compared to
well established approaches on a reference data set, and obtains better rates
of positive and negative controls. However, many signals are not detected by
the proposed method. So, we conclude that this method should be used in
parallel to existing measures in pharmacovigilance.
