报告主题：A Bayesian latent-subgroup platform design for dose optimization

时间：2025年5月21日14:30-15:30

地点：上川路校区一教218 

报告人：牟荣吉

报告内容简介：

The US Food and Drug Administration (FDA) launched Project Optimus to reform the dose optimization and dose selection paradigm in oncology drug development, calling for the paradigm shift from finding the maximum tolerated dose to the identification of optimal biological dose (OBD). Motivated by a real-world drug development program, we propose a master-protocol-based platform trial design to simultaneously identify OBDs of a new drug, combined with standards of care or other novel agents, in multiple indications. We propose a Bayesian latent subgroup model to accommodate the treatment heterogeneity across indications, and employ Bayesian hierarchical models to borrow information within subgroups. At each interim, we update the subgroup membership and dosetoxicity and -efficacy estimates, as well as the estimate of the utility for risk-benefit tradeoff, based on the observed data across treatment arms to inform the arm-specific decision of dose escalation and de-escalation and identify the optimal biological dose for each arm of a combination partner and an indication. The simulation study shows that the proposed design has desirable operating characteristics, providing a highly flexible and efficient way for dose optimization. The design has great potential to shorten the drug development timeline, save costs by reducing overlapping infrastructure, and speed up regulatory approval.

主讲人简介：

牟荣吉，理学博士，上海交通大学医学院助理研究员。2017年毕业于华东师范大学，主要研究方向为贝叶斯适应性设计以及生物统计。近年来在国际知名期刊发表论文20余篇包括Biometrics, Journal of the Royal Statistical Society: Series C, Statistics in Medicine等统计学知名期刊以及Cancer Cell，Cancer Communications等医学顶刊。主持国家自然科学基金青年项目，博士后面上项目，上海交通大学“交大之星”计划医工交叉研究基金以及横向项目等。