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We propose a test-based elastic integrative analysis of the randomized trialand real-world data to estimate treatment effect heterogeneity with a vector ofknown effect modifiers. When the real-world data are not subject to bias, ourapproach combines the trial and real-world data for efficient estimation.Utilizing the trial design, we construct a test to decide whether or not to usereal-world data. We characterize the asymptotic distribution of the test-basedestimator under local alternatives. We provide a data-adaptive procedure toselect the test threshold that promises the smallest mean square error and anelastic confidence interval with a good finite-sample coverage property.

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