报告题目：A Bayesian seamless phase I-II design to accelerate the development of targeted therapies and immunotheraphy        

报告时间：2021年10月29日下午20：00

会议链接：https://meeting.tencent.com/dm/0ef0G32VLsJN

会议 ID：278 887 437

主办单位：科研处/数学与统计学院

主讲人：言方荣

言方荣简介：窗言方荣简介：博士，教授，博士生导师，中国药科大学理学院生物统计教研室主任，生物统计与计算药学研究中心主任，美国MD Anderson癌症研究中心生物统计系访问学者，并兼任中国医药教学协会医药统计专业委员会副主任委员。主要研究领域包括：临床试验中的生物统计问题，自适应试验设计，生存分析与肿瘤精准治疗，癌症基因组学分析，群体药物代谢动力学分析及药学实验数据建模和分析，生物医药大数据及医疗大数据分析理论及应用.近年来在国内外以第一作者或通讯作者发表学术论文70多篇，单篇SCI影响因子最高74.69，代表论文包括医学及肿瘤学顶级刊物NEJM，Annals of Oncology , Clin Cancer Re (discussion paper), Genome Biology，AJRCCM，生物统计权威刊物Journal of Statistical Software, The Annals of Applied Statistics，SMMR，JRSSC，Bioinformatics, Statistics in Medicine, Pharmaceutical Statistics等，入选江苏省“六大人才”高峰项目、江苏省“青蓝工程”中青年学术带头人.现主持国家自然科学基金面上项目1项，国家社科基金面上项目1项，省部级课题3项，横向课题多项，构建临床数据库多个，开发完成系列临床试验实用软件，出版肿瘤临床试验方法学专著《肿瘤临床试验贝叶斯设计方法》。主持江苏省研究生优秀课程1项，江苏省研究生教改课题1项，省留学生精品培育课程及精品课程各1项，校重点建设课程1项。主持完成校级教改课题1项，作为主要完成人，获省精品课程2项（排第三），校教学成果一等奖、二等奖各一项（排名第二），主编出版教材5部，作为负责人完成。

窗体底端

报告摘要：Drug development of novel antitumor agents is conventionally divided by phase and cancer indication. With the advent of new molecularly targeted therapies and immunotherapies, this approach has become inefficient and dysfunctional. We propose a Bayesian seamless phase I-II “shotgun” design to evaluate the safety and antitumor efficacy of a new drug in multiple cancer indications simultaneously. The shotgun design begins with an all-comer dose finding phase to identify the maximum tolerated dose (MTD) or recommended phase II dose (RP2D) before seamlessly moving to indication-specific cohort expansions. Patients treated during dose-finding are rolled over to the cohort expansion for more efficient evaluation of efficacy, while patients enrolled in cohort expansion contribute to the continuous learning of the safety and tolerability of the new drug. During cohort expansion, interim analyses are performed to discontinue ineffective or unsafe expansion cohorts early. To improve the efficiency of such interim analyses, we propose a clustered Bayesian hierarchical model to adaptively borrow information across indications. A simulation study shows that compared to conventional approaches and the standard Bayesian hierarchical model, the shotgun design has substantially higher probabilities to discover indications that are responsive to the treatment in question, and is associated with a reasonable false discovery rate. The shotgun provides a phase I-II trial design for accelerating drug development and to build a more robust foundation for subsequent phase III trials.