In drug safety, development of statistical methods for multiplicity adjustments has exploited potential relationships among adverse events (AEs) according to underlying medical features. Due to the coarseness of the biological features used to group AEs together, which serves as the basis for the adjustment, it is possible that a single adverse event can be simultaneously described by multiple biological features. However, existing methods are limited in that they are not structurally flexible enough to accurately exploit this multi-dimensional characteristic of an adverse event. In order to preserve the complex dependencies present in clinical safety data, a Bayesian approach for modeling the risk differentials of the AEs between the treatment and comparator arms is proposed which provides a more appropriate clinical description of the drug's safety profile. The proposed procedure uses an Ising prior to unite medically related AEs. The proposed method and an existing Bayesian method are applied to a clinical dataset, and the signals from the two methods are presented. Results from a small simulation study are also presented.
Keywords: Bayes Factor; Drug safety; High-dimensional data analysis; Ising prior; Markov random field; MedDRA.
© 2013, The International Biometric Society.