2020年，《真实世界证据支持药物研发与审评指导原则（试行）》出台，作为国药监2020年的一号文件，为国内真实世界证据支持药品研发与审评奠定了里程碑。事实上，真实世界在中国临床研究领域已不再是个新名词。近年来，我们见证了海南博鳌真实世界研究快速通道、罕见病药物研发以及临床联合用药，在利用真实世界证据评价药物有效性和安全性上做了各种实践，加速了传统药物研发并有效提高了研发的成功率。然而，我们的征程不止于此。中国在真实世界需开辟自己的原研创新，如何从国内浩瀚的真实世界数据中挖掘并提炼出完整的证据链，利用真正硬核的方法学创新汇成稳健的真实世界证据，进而推动真实世界研究在国内快速发挥作用，是摆在每一位生物统计人面前的新挑战。
2022上海生物统计论坛（SBF）第二季度研讨会将于2022年6月30日在线举行。此次会议主题为“利用真实世界证据为决策提供信息：机遇与挑战 Empowering Decision-making with RWE: Challenges and Opportunities”，艾伯维医学事务与卫生技术评估统计负责人Dr. Weili He，复旦大学附属中山医院黄丽红研究员，勃林格殷格翰流行病学部门负责人Dr. Qiang Li，欧洲杯竞猜平台刘林副教授将围绕真实世界证据和与会者共同探讨当前生物统计及临床试验领域最炙手可热的议题。
本次论坛将于线上举行。在线参与信息将在会议前一天通过在线报名时所填写的邮箱发至参会者，请及时查收。
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艾伯维 医学事务及卫生技术评估统计负责人
Dr. Weili He is the head of Medical Affairs and Health Technology Assessment (MA&HTA) Statistics at AbbVie Inc. She has a PhD in biostatistics. Prior to AbbVie, she worked at Merck & Co., Inc. for over 20 years. Weili has published extensively in the areas of adaptive designs (AD), benefit-risk assessment (BRA), and RWE methodology research. She is the lead or co-author of more than 50 peer-reviewed publications in statistical and medical journals and the lead Editor of two books on AD and BRA. Since joining AbbVie in February 2017, Weili has involved extensively in RWD and RWE research for evidence generations. She is the co-founder and co-chair of the ASA Biopharm Section (BIOP) RWE SWG since 2018. The SWG’s efforts in the past 4+ yeas resulted in three peer-review publications in a statistical journal and 3 additional manuscripts in near final stage submitted or to be submitted. Weili is also the BIOP Chair 2021, an AE for the journal of the SBR since January 2014, and an elected Fellow of the ASA.
报告主题：Key Considerations to Address Key Challenges in the Uptake of RWE for Regulatory Decisions
复旦大学附属中山医院 研究员
黄丽红，复旦大学附属中山医院青年研究员。博士毕业于南京医科大学，获流行病与卫生统计学博士，美国俄亥俄州立大学博士后。复旦大学公共卫生学院研究生导师（生物统计方向）。主要研究领域：临床研究中的统计理论方法与规范化。CSCO生物统计专家委员会专家委员、中国真实世界数据与研究联盟（ChinaREAL）专家成员、国家药品监督管理局海南真实世界数据研究与评价重点实验室特聘专家。个人专长：临床试验统计设计与分析，real world study评价与分析、临床试验信息化系统，大于10年临床试验相关经验。全国统计“十四五”规划教材《医学统计学》编委、《临床试验统计学》学术秘书、参编《临床试验精选案例统计学解读》等。
报告主题：临床研究设计与分析中的因果推断元素
勃林格殷格翰 流行病学部门负责人
Dr. Li is currently the regional epidemiology lead (Asia) in Boehringer Ingelheim (China) which he has been with since 2014. Prior to BI, he served as a director in the division of surveillance, tobacco control office, the Chinese Center for Disease Control and Prevention. He holds a PhD degree in epidemiology from State University of New York at Buffalo.
报告主题：Use of Real World Evidence to Support Regulatory Decision Making
报告摘要
In recent years, there has been a rapid increase in the opportunities to use real world evidence (RWE) to support drug development throughout the drug development lifecycle. In the pre-launch setting, RWE can be used to refine understanding of target patient populations and unmet medical needs, to inform clinical development, and to demonstrate and negotiate value to various stakeholders. In the post-launch setting, RWE not only can be applied in traditional activities such as post-approval safety studies, but also be used to further understand patient populations, to confirm and monitor benefit-risk, and to better manage the drug lifecycle (e.g., line extension or new indication). This talk will focus on the opportunities of using RWE to support regulatory decision making with a few use cases.
欧洲杯竞猜平台 副教授
刘林在欧洲杯竞猜平台自然科学研究院、数学科学学院、交大-耶鲁生物统计与数据科学中心任职长聘教轨副教授。研究方向为非参数半参数统计理论、因果推断、机器学习、及统计学在生物医学中的应用。
报告主题：When Should We Not Trust Black-box Machine Learning in Causal Inference?
报告摘要
In the era of AI and big data, black-box machine learning or deep learning is becoming the default tool for practicing data analysts or applied (bio)statisticians. As a part of our profession, we, the statisticians, should be responsible for providing at least some guidance on when we should or should not trust the result outputted by deep learning. In this talk, I will use the average treatment effect estimation in causal inference as an example, demonstrating that our current theoretical understanding of deep learning is far from guiding practice. As a remedy, we developed a fully data-driven, assumption-lean diagnostic tool to deep learning-based causal effect estimation. We demonstrate the practical value of our new tool in both synthetic data, partially synthetic data generated using W-GAN to mimic the real data, and a real dataset from NCDB. This talk is based on three papers, one with Siqi Xu and Zhonghua Liu from HKU, one with Zhenyu Liao (HUST) and Chang Li (a senior math major at SJTU), and another with Kerollos Wanis and James Robins.
本次SBF Q2论坛由欧洲杯竞猜平台临床研究中心、勃林格殷格翰主办，上海预防医学会卫生统计专业委员会协办。
论坛由欧洲杯竞猜平台临床研究中心和勃林格殷格翰牵头组织，由欧洲杯竞猜平台临床研究中心主任俞章盛、勃林格殷格翰大中华区生物统计和数据科学负责人买亚兵任论坛筹备主席，论坛筹备委员会成员包括欧洲杯竞猜平台临床研究中心牟荣吉博士、勃林格殷格翰邓红洁博士、勃林格殷格翰卓斌博士，并由欧洲杯竞猜平台临床研究中心顾雯韵老师负责论坛秘书处各项工作的组织开展，共同统筹本届论坛各项筹备工作推进，保障论坛顺利召开。
