Shu Yang: Test-based integrative analysis for combining randomized trial and real-world data

Test-based integrative analysis for heterogeneous treatment effects combining randomized trial and real-world data
Discussant: Issa Dahabreh (Harvard University)
Abstract: 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 a test-based elastic integrative analysis of 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 bias, e.g., due to hidden confounding, our approach combines the RT and RW data 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 RW data and decide whether or not to use RW data 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. If time permits, I will cover other approaches such as using the notion of the confounding function to improve inference for HTEs using RT and RW data. Keywords: Bias function; Least favorable confidence interval; Nonregularity; Pre-test estimator; Semiparametric efficient score.