报告题目：Test-Then-Pool: A Uniformly Valid Inferential Framework for Data Integration

报告时间：2023-07-06  10:00 - 11:30

报告人：Prof. Yang Shu, North Carolina State University

报告地点：理学院东北楼四楼报告厅

Abstract: 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.

Paper #1: S. Yang, C. Gao, X. Wang, and D. Zeng (2022). Elastic integrative analysis of randomized trial and real-world data for treatment heterogeneity estimation. Journal of the Royal Statistical Society: Series B.

Paper #2: C. Gao and S. Yang (2023). Pretest estimation in combining probability and non-probability samples. Electronic Journal of Statistics.