​Elastic Integrative Analysis of Randomized Trial and Real-World Data for Treatment Heterogeneity Estimation

Parallel randomized trial (RT) and real-world (RW) data are becoming increasingly available for treatment evaluation. Given the complementary features of the RT and RW data, we propose an elastic integrative analysis of the RT and RW data for accurate and robust estimation of the heterogeneity of treatment effect (HTE), which lies at the heart of precision medicine. When the RW data are not subject to unmeasured confounding, our approach combines the RT and RW data for optimal estimation by exploiting the semiparametric efficiency theory. The proposed approach also automatically detects the existence of unmeasured confounding in the RW data and gears to the RT data. Utilizing the design advantage of RTs, we are able to gauge the reliability of the RW data and decide whether or not to use RW data in an integrative analysis. The advantage of the proposed research lies in integrating the RT and big RW data seamlessly for consistent HTE estimation. We apply the proposed method to characterize who can benefit from adjuvant chemotherapy in patients with stage IB non-small cell lung cancer.

Shu Yang is an assistant professor of Statistics at NC 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. A postdoc position is open: https://jobs.ncsu.edu/postings/132863. See more information at https://shuyang.wordpress.ncsu.edu/.