Bayesian Approach to Personalized Benefit-Risk Assessment

ABSTRACTBenefit-risk assessment is critical in evaluating the effectiveness of a new drug before and after the approval. Some benefit-risk measures depend on the probabilities of benefit-risk categories in which the subject-level benefit and risk outcomes are characterized. The existing benefit-risk methods for analyzing the categorical data depend only on the frequencies of mutually exclusive and collectively exhaustive categories that the subjects fall in, and thus ignore the subject-level differences. We propose a Bayesian method for analyzing the subject-level data with multiple visits. A generalized linear model is used to model the subject-level response probability, with respect to a “reference” category, assuming a logit model with subject-level category effects and multiple visit effects. The random longitudinal visit effects are modeled by a multivariate normal distribution with zero means and first-order autoregressive structured variance-covariance matrices. In the proposed Bayesian setup, a D...