Event Summary:
A one-day workshop organized by the Center for Interdisciplinary Research and
Consulting at the University of Maryland, Baltimore County (UMBC), and the
Center for Innovative Study Design (CISD) at Stanford University in the field of Clinical Trials and Regulatory Science.
Registration is now closed
⇒View Archives here⇐
Organizing Committee:
Faculty from UMBC and Stanford University and leaders from FDA and industry.
For more information:
Please contact Dr. Yi Huang, Department of Mathematics and
Statistics, University of Maryland Baltimore County (UMBC),
1000 Hilltop Circle, Baltimore, MD 21250.
E-Mail: yihuang@umbc.edu.
For any Technical difficulties Please contact Zana Coulibaly at czana1@umbc.edu.
Participant Information
Yueqin Zhao from FDA.
Paper: Bayesian Approaches for Benefit-Risk Assessment
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. After we review the current development of Bayesian methods for benefit-risk assessment, we propose a new 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.