A Bayesian Approach for Benefit-Risk Assessment

An important aspect of the drug evaluation process is to have an integrated benefit-risk assessment to determine, using some quantitative measures, whether the benefit outweighs the risk for the target population. Chuang-Stein et al. proposed a five-category random variable along with three global measures of benefit-risk assessment. Assuming the cell probabilities follow a multinomial distribution, we propose a Bayesian approach for the longitudinal assessment of benefit-risk using these three global measures and a new measure. A Dirichlet distribution is used as the natural conjugate prior for multinomial cell probabilities, and the posterior distributions of cell-probabilities are recursively derived as the data from multiple visits become available. In a more generalized approach, a power prior is used through the likelihood function to discount the information from previous visits, and, again, the posterior distributions of the cell-probabilities at multiple visits are derived. The estimates of the posterior means and credible intervals for the four global measures are derived, and the decision rules based on the credible intervals are applied for the assessment of the four global measures. Using two simulated datasets generated under two different scenarios—one where benefit outweighs risk and the other where benefit does not outweigh risk—the performances of the four measures are evaluated using a Markov chain Monte Carlo (MCMC) technique. We illustrate of the methodology using clinical trial data.