Bayesian Sequential Analysis of Adverse Events with Binary Data

We propose a Bayesian sequential procedure to test hypotheses concerning the relative risk between two specific treatments based on the binary data obtained from the two-arm clinical trial. Our development is based on an optimal sequential test cast within the Bayesian framework. This approach enables us to provide, in a straightforward manner based on the Stopping Rule Principle (SRP), an assessment of the various error probabilities via posterior probabilities and conditional error probabilities. An attractive feature of our approach is the relative simplicity of the calculations involved without having to resort to cumbersome iterative methods of `spending the alpha’ and the like. The proposed methods are illustrated using a sequential safety study of adverse events following H1N1 influenza vaccination and are compared with existing sequential approaches. The findings demonstrate that the proposed Bayesian framework provides an efficient and flexible approach for the early detection of adverse events under several different prior distributions of the parameters involved.