报告题目：Multiply robust subgroup analysis based on a single-index threshold linear marginal model for longitudinal data with dropouts
报告人：秦国友 教授（复旦大学）
报告时间： 2022年5月25日（星期三）15:30-16:30
报告地点： 腾讯会议（ID：336 340 085）
校内联系人：牛一 副教授 联系方式：niuyi@dlut.edu.cn
报告摘要: Identifying subpopulations that may be sensitive to the specific treatment is an important step toward precision medicine. On the other hand, longitudinal data with dropouts is common in medical research, and subgroup analysis for this data type is still limited. In this paper, we consider a single-index threshold linear marginal model, which can be used simultaneously to identify subgroups with differential treatment effects based on linear combination of the selected biomarkers, estimate the treatment effects in different subgroups based on regression coefficients, and test the significance of the difference in treatment effects based on treatment-subgroup interaction. The regression parameters are estimated by solving a penalized smoothed generalized estimating equation and the selection bias caused by missingness is corrected by a multiply robust weighting matrix, which allows multiple missingness models to be taken account into estimation. The proposed estimator remains consistent when any model for missingness is correctly specified. Under regularity conditions, the asymptotic normality of the estimator is established. Simulation studies confirm the desirable finite-sample performance of the proposed method. As an application, we analyze the data from a clinical trial on pancreatic cancer.
报告人简介：秦国友，博士，美国北卡罗莱纳大学教堂山分校博士后。现为复旦大学教授、博士生导师，公共卫生学院生物统计学教研室主任。主要从事生物统计学方法学和应用研究，包括真实世界研究和因果推断，针对复杂数据、复杂统计模型的统计方法和理论创新，以及生物统计学方法在医学和公共卫生领域的创新应用。纵向数据分析相关研究工作获得了2014年教育部高等学校科学研究优秀成果二等奖。在BMJ、Plos Medicine、Biometrics、Biostatistics和Statistics in Medicine等医学和生物统计顶级期刊上发表论文100余篇，先后主持四项国家自然科学基金项目。主要学术兼职：中华预防医学会生物统计学分会第一届青年委员会主任委员，中国卫生信息学会健康统计专业委员会、中华预防医学会生物统计分会和中华预防医学会健康保险分会常务委员以及《中国卫生统计》编委。
