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

TL;DR

This paper introduces a test-based elastic integrative analysis method that combines randomized trial and real-world data to accurately estimate treatment heterogeneity, with adaptive testing and confidence intervals for improved inference.

Contribution

It presents a novel test-based framework for integrating trial and real-world data, including adaptive threshold selection and asymptotic distribution characterization.

Findings

Efficient estimation of treatment heterogeneity using combined data.

Adaptive procedure minimizes mean square error.

Elastic confidence intervals with good finite-sample coverage.

Abstract

We propose a test-based elastic integrative analysis of the randomized 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. Utilizing the trial design, we construct a test to decide whether or not to use real-world data. We characterize 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.