Empower RCT Analysis with Integrated Information from RWD

When and Where

Thursday, April 13, 2023 3:30 pm to 4:30 pm
Room 9014
Ontario Power Building
700 University Avenue, Toronto, ON M5G 1Z5


Shu Yang, North Carolina State University


Parallel randomized clinical trial (RCT) and real-world data (RWD) are becoming increasingly available for treatment evaluation. Given the complementary features of the RCT and RWD, I first discuss the common questions that can be answered by integrative analysis of RCT and RWD (Yang and Wang, 2022; Colnet et al, 2022). Then, I will present a test-based elastic integrative analysis of RCT and RWD for accurate and robust estimation of the heterogeneity of treatment effect (HTE), which lies at the heart of precision medicine. When the RWD are not subject to bias, e.g., due to hidden confounding, our approach combines the RCT and RWD for optimal estimation by exploiting semiparametric efficiency theory. 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. A data-adaptive procedure is proposed to select the threshold of the test statistic that promises the smallest mean square error of the proposed estimator of the HTE. Lastly, 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.

Paper #1: S. Yang and X. Wang (2022). RWD-integrated randomized clinical trial analysis. 2022 ASA Biopharmaceutical Report Real World Evidence (Editors: Herbert Pang, Ling Wang, Kristi L. Griffiths), 29, 15–21.
Paper #2: B. Colnet, I. Mayer, G. Chen, A. Dieng, R. Li, G. Varoquaux, J.P. Vert, J. Josse, S. Yang (2022). Causal inference methods for combining randomized trials and observational studies: a review.
Paper #3:  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, accepted.

Please join the event.

About Shu Yang

Picture of Shu YangShu Yang 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 Principal Investigator for several U.S. National Science Foundation and National Institute of Health research projects.



700 University Avenue, Toronto, ON M5G 1Z5