光华讲坛——社会名流与企业家论坛第 期

主 题：A new joint model of a longitudinal outcome and a competing risks time-to-event outcome纵向结果和具有竞争风险的时间-事件结果的新联合模型

主讲人：美国加州大学洛杉矶分校(UCLA) 李刚教授

主持人：统计学院林华珍教授

时间：2023年6月29日（周四）下午16:00-17:00

举办地点：柳林校区弘远楼408会议室

主办单位：统计研究中心和统计学院 科研处

主讲人简介：

Dr. Gang Li is Professor of Biostatistics and Computational Medicine at University of California at Los Angeles (UCLA). He serves as the Director of the Biostatistics Shared Resource at UCLA's Jonsson Comprehensive Cancer Center. Dr. Li is an Elected Fellow of the Institute of Mathematical Statistics, the American Statistical Association, and the Royal Statistical Society, and an Elected Member of the International Statistical Institute. Dr. Li is co-Editor in Chief (2022-2024) for the Electronic Journal Statistics published by the Institute of Mathematical Statistics and the Bernoulli Society. Additionally, he currently serves as the President (2022-2024) of International Chinese Statistical Association. Dr. Li’s research encompasses a broad range of areas, including survival analysis, longitudinal data analysis, high dimensional data analysis, clinical trials, and high performance statistical computing for large-scale electronic health records (EHR) and biobank data. He has made significant contributions to these fields and has co-authored/edited three research monographs. Furthermore, his work has been published in over 150 peer-reviewed papers, many of which are featured in renowned journals such as the Annals of Statistics, Journal of the American Statistical Association, and Journal of the Royal Statistical Society-B. In addition to his methodological research, Dr. Li actively engages in collaborative research in basic science, translational science, and clinical trials. He has served as the Principal Investigator for numerous studies funded by the National Institutes of Health (NIH) and the National Science Foundation (NSF).

李刚博士是美国加州大学洛杉矶分校(UCLA)生物统计学和计算医学教授。他担任加州大学洛杉矶分校Jonsson综合癌症中心生物统计共享资源主任。他是数理统计协会(IMS)、美国统计协会(ASA)、皇家统计学会和国际统计学会（ISI）的Elected Fellow。他是数理统计协会和伯努利学会出版的电子期刊统计学的总编辑(2022年至2024年)。此外，他还担任泛华统计协会（ICSA）主席(2022-2024)。他的研究涵盖了广泛的领域，包括生存分析、纵向数据分析、高维数据分析、临床试验和大规模电子健康记录(EHR)和生物银行数据的高性能统计计算。他在这些领域做出了重大贡献，并与人合著/编辑了三本研究专著。此外，他发表了150多篇论文，其中许多论文发表在著名期刊上，如AoS、JASA和JRSSB。除了方法论研究外，他还积极参与基础科学、转化科学和临床试验方面的合作研究。他曾担任由美国国立卫生研究院(NIH)和美国国家科学基金会(NSF)资助的多项研究的Principal Investigator。

内容简介：

Recent discoveries have emphasized the importance of within-subject (WS) visit-to-visit variability of longitudinal biomarkers as significant risk factors for health outcomes. This talk introduces a novel joint model that incorporates a longitudinal biomarker with heterogeneous WS variability and a competing risks time-to-event outcome. The proposed model provides a valuable framework for testing heterogeneity in WS variability, exploring the association between WS variability and survival outcomes, and enabling dynamic prediction of survival by considering both the individual mean and WS variability of the biomarker. We present an expectation-maximization algorithm for semiparametric maximum likelihood estimation, along with a profile-likelihood method for standard error estimation and inference. Moreover, we have developed efficient computational algorithms specifically tailored for analyzing biobank-scale data with tens of thousands of subjects. Through simulation results, we demonstrate the advantages of our method over traditional joint models. To illustrate the practical implications of our approach, we apply it to the Multi-Ethnic Study of Atherosclerosis (MESA) data, yielding intriguing findings.

最近的发现强调了纵向生物标志物作为健康结果的重要风险因素在受试者自身(WS)访间变异性的重要性。本次报告介绍了一种新的联合模型，该模型结合了具有异质性WS变异性和具有竞争风险的时间-事件结果的纵向生物标志物。所提出的模型提供了一个有价值的框架，能够检验WS变异性的异质性，探索WS变异性与生存结果之间的关系，以及通过考虑生物标志物的个体平均值和WS变异性来动态预测生存状况。主讲人提出了一种半参数最大似然估计的期望最大化算法，以及一种针对标准误差估计和推断的轮廓似然方法。此外，还开发了高效的计算算法，专门用于分析具有数万受试者的生物库规模的数据。模拟结果表明，该方法优于传统的联合模型。为了说明该方法的实际意义，主讲人将其应用于动脉粥样硬化的多种族研究(MESA)数据，得到了有趣的发现。