Bayesian approach to personalized benefit-risk assessment with application to a clinical trial data
*Ram Tiwari, Food and Drug Administration 

Keywords: Dirichlet process

Benefit-risk assessment is critical in evaluating the effectiveness of a new treatment over the existing ones. 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 categorical data with multiple visits. A generalized linear model is used to model the subject-level response probability of each category, with respect to a “reference” category, assuming a logit model with subject-level category effects and multiple visit effects. Dirichlet process is used as a prior for the subject-level category effects to catch the similarity among the subject responses. We develop an efficient Markov chain Monte Carlo algorithm for implementing the proposed method, and illustrate the estimation of individual benefit-risk profiles through a simulation study. A clinical trial data is analyzed using the proposed method to assess the subject-level or personalized benefit-risk in each arm, and to evaluate the aggregated benefit-risk difference between the treatments at different visits.