A Bayesian approach to benefit-risk assessment in clinical studies with longitudinal data

Chuang-Stein et al. proposed a method for benefit-risk assessment by formulating a five-category multinomial random variable with the first four categories as a combination of benefit and risk, and the fifth category to include subjects who withdraw from study. In this article, we subdivide the single withdrawal category into four sub-categories to consider withdrawal for different reasons. To analyze eight-category data, we propose a two-level multivariate-Dirichlet Model to identify benefit-risk measures at the population level. For individual benefit-risk, we use a log-odds ratio model with Dirichlet process prior. Two methods are applied to a hypothetical clinical trial data for illustration.