Shu Yang (North Carolina State University)
Title: Test-based integrative analysis of randomized trial and real-world data for treatment heterogeneity estimation.
Abstract:
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, we propose a test-based integrative analysis of the 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 unmeasured confounding, our approach combines the RCT and RWD for optimal estimation by exploiting semiparametric efficiency theory. Utilizing the design advantage of RTs, 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. We characterize the asymptotic distribution of the test-based integrative estimator under local alternatives, which provides a better approximation of the finite-sample behaviors of the test and estimator when the idealistic assumption required for the RWD is weakly violated. We provide a data-adaptive procedure 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.
Speaker
Shu Yang is an assistant professor in the Department of Statistics at The North Carolina State University. Her research is mainly about causal inference in longitudinal observational data, semiparametric efficient estimation, missing data analysis and imputation methods. She obtained her PhD in statistics from Iowa State University in 2014.
Meeting ID: 924 5390 4989
Passcode: 690084
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