A matching design for augmenting a randomized clinical trial with external control

TL;DR

This paper proposes an improved matching design for hybrid randomized controlled trials that combines internal RCT data with external real-world data, ensuring better comparability and data integrity.

Contribution

It introduces a novel matching approach that aligns external control subjects with RCT populations, applicable to multi-arm trials, and allows pre-unblinding matching to enhance data integrity.

Findings

The proposed method achieves better comparability between external controls and RCT participants.

Simulation results demonstrate improved estimator performance and variance estimation.

The approach maintains data integrity by enabling pre-unblinding matching.

Abstract

The use of information from real world to assess the effectiveness of medical products is becoming increasingly popular and more acceptable by regulatory agencies. According to a strategic real-world evidence framework published by U.S. Food and Drug Administration, a hybrid randomized controlled trial that augments internal control arm with real-world data is a pragmatic approach worth more attention. In this paper, we aim to improve on existing matching designs for such a hybrid randomized controlled trial. In particular, we propose to match the entire concurrent randomized clinical trial (RCT) such that (1) the matched external control subjects used to augment the internal control arm are as comparable as possible to the RCT population, (2) every active treatment arm in an RCT with multiple treatments is compared with the same control group, and (3) matching can be conducted and the matched set locked before treatment unblinding to better maintain the data integrity. Besides a weighted estimator, we also introduce a bootstrap method to obtain its variance estimation. The finite sample performance of the proposed method is evaluated by simulations based on data from a real clinical trial.