标题：动态追踪疾病状态的建模与分析
报告时间：2025年8月2日（星期六）13：20-14：20
报告地点：人民大街校区数学与统计学院415教室
主讲人：李启寨
主办单位：数学与统计学院
报告内容简介：
Tracking health outcomes in longitudinal biomedical studies has important clinical implications in preventive medicine, which is methodologically challenging because of time-varying covariates and unbalanced longitudinal data. A tracking index, the ``tracking prediction probability" (TPP), is proposed to quantify the ability of a subject developing certain health outcomes conditioning on his\her health outcomes and covariates at a past time point. For the unknown unstructured distribution functions in practice, we consider a class of structured dynamic copula-based models for the TPPs at two time points, and give the continuous-time TPP estimates of TPP through local smoothing allowing for the known or unknown copula family. They can provide additional insights into the dynamic patterns of the disease risk factors and the health outcomes. We develop the asymptotic properties of the discrete-time and continuous-time estimators for the copula parameters and the TPPs, and investigate the finite sample properties of these estimators through a comprehensive simulation. Applying our models and estimation procedure to a large epidemiological study of child growth and cardiovascular health, the dynamic relationships between the risk of developing hypertension in the future and the evolving patterns of past blood pressure levels and BMI changes are efficiently illustrated.
主讲人简介：
中国科学院数学与系统科学研究院 研究员，系统科学研究所副所长；2001年本科毕业于中国科学技术大学，2006年博士毕业于中国科学院数学与系统科学研究院，2006-2009年在美国国立卫生健康研究院(NIH)国家癌症研究所(NCI)从事博士后研究；2016年当选国际统计学会推选会员(ISI Elected Member), 2020年当选美国统计学会会士(ASA Fellow)，2023年获国家高端人才称号；研究方向：生物医学统计、遗传统计、复杂数据推断等；在Nature Genetics, Science Advances, ACIE, CR, AJHG, Bioinformatics, IEEE TPAMI, Psychometrika, JASA, JRSSB, Biometrics等期刊发表论文120余篇；现任中国数学会常务理事、中国现场统计研究会常务理事等。
