Description Usage Arguments Value Author(s) References

View source: R/Analyze_oneAE.R

In this function, 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

1 | ```
Analyze_oneAE(ae, drug, maxit, alpha, nbinit)
``` |

`ae` |
Binary vector indicate if individual suffers from adverse event (1) or no (0). |

`drug` |
Matrix of drugs consumptions. Each row corresponds to one individual drug consumptions. |

`maxit` |
Numeric indicating the number of iterations. |

`alpha` |
Numeric indicating the size of the neighborhood where the proposal will be uniformly sampled at each iteration. See~http://arxiv.org/abs/1505.03366 for more details. |

`nbinit` |
The number of random initializations of the algorithm. |

List of detected signals.

Matthieu Marbac and Mohammed Sedki

Matthieu Marbac, Pascale Tubert-Bitter, Mohammed Sedki

Bayesian model selection in logistic regression for the detection of adverse drug reactions~http://arxiv.org/abs/1505.03366

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