报告题目: Test-Then-Pool: A uniformly valid inferential framework for data integration
报告人：杨淑 (北卡罗来纳州立大学)
报告时间：2023年7月5日15:30-16:30
报告地点：文波楼401
报告摘要：Parallel randomized clinical trial (RCT) and real-world data (RWD) are becoming increasingly available for treatment evaluation. Test-Then-Pool (TTP) analysis of RCT and RWD is a natural idea for accurate and robust estimation of the treatment effect. When the RWD are not subject to bias, e.g., due to hidden confounding, our approach combines the RCT and RWD for optimal estimation. Utilizing the design advantage of RCTs, we construct a built-in test procedure to gauge the reliability of the RWD and decide whether or not to use RWD in an integrative analysis. The TTP estimator belongs to pre-testing estimators and is non-regular. Consequently, standard fixed-parameter asymptotics provide poor approximation to the finite sample distribution. We resort to local-parameter asymptotics to faithfully capture non-regularity as sample size grows large. Finally, we construct an adaptive confidence interval that has a good finite-sample coverage property. We apply the proposed method to characterize who can benefit from adjuvant chemotherapy in patients with stage IB non-small cell lung cancer based on RCT and RWD cohorts.
主讲人简介：Shu Yang （杨淑）is Associate Professor of Statistics, Goodnight Early Career Innovator, and University Faculty Scholar at North Carolina State University. She received her Ph.D. in Applied Mathematics and Statistics from Iowa State University and postdoctoral training at Harvard T.H. Chan School of Public Health. Her primary research interest is causal inference and data integration, particularly with applications to comparative effectiveness research in health studies. She also works extensively on methods for missing data and spatial statistics. She has been co-Investigator for a Patient-Centered Outcomes Research Institute grant and Principal Investigator for U.S. National Science Foundation and National Institute of Health research projects.
