Improving randomized controlled trial analysis via data-adaptive borrowing

TL;DR

This paper introduces a data-adaptive framework for integrating external controls into randomized trials, which dynamically selects comparable data to reduce bias and improve estimation accuracy.

Contribution

It proposes a novel adaptive method that balances efficiency and bias mitigation, with theoretical guarantees and demonstrated superior performance in simulations and real data.

Findings

Achieves semiparametric efficiency when external controls are comparable

Effectively reduces bias from incomparable external controls

Improves estimation accuracy over trial-only methods

Abstract

In recent years, real-world external controls have grown in popularity as a tool to empower randomized placebo-controlled trials, particularly in rare diseases or cases where balanced randomization is unethical or impractical. However, as external controls are not always comparable to the trials, direct borrowing without scrutiny may heavily bias the treatment effect estimator. Our paper proposes a data-adaptive integrative framework capable of preventing unknown biases of the external controls. The adaptive nature is achieved by dynamically sorting out a comparable subset of the external controls via bias penalization. Our proposed method can simultaneously achieve (a) the semiparametric efficiency bound when the external controls are comparable and (b) selective borrowing that mitigates the impact of the existence of incomparable external controls. Furthermore, we establish statistical guarantees, including consistency, asymptotic distribution, and inference, providing type-I error control and good power. Extensive simulations and two real-data applications show that the proposed method leads to improved performance over the trial-only estimator across various bias-generating scenarios.