Elastic integrative analysis of randomised trial and real-world data for treatment heterogeneity estimation

We propose a test-based elastic integrative analysis of the randomised trial and real-world data to estimate treatment effect heterogeneity with a vector of known effect modifiers. When the real-world data are not subject to bias, our approach combines the trial and real-world data for efficient estimation. Utilising the trial design, we construct a test to decide whether or not to use real-world data. We characterise the asymptotic distribution of the test-based estimator under local alternatives. We provide a data-adaptive procedure to select the test threshold that promises the smallest mean square error and an elastic confidence interval with a good finite-sample coverage property.

Automated summary

We propose a test-based elastic integrative analysis that combines randomised trial and real-world data to estimate treatment effect heterogeneity. Our approach allows for efficient estimation by using the trial design to decide whether to incorporate real-world data. We provide a data-adaptive procedure for selecting a test threshold that minimises mean square error and offer an elastic confidence interval with good finite-sample coverage.