Consistency Assessment of Regional Treatment Effect for Multi-Regional Clinical Trials in the Presence of Covariate Shift

TL;DR

This paper introduces a new method for assessing treatment effect consistency across regions in multi-regional clinical trials, accounting for covariate differences that can bias traditional evaluations.

Contribution

It proposes a two-step assessment strategy that explicitly considers covariate-driven heterogeneity in treatment effects, improving accuracy over existing methods.

Findings

The proposed method effectively mitigates bias from covariate distribution differences.

Numerical studies confirm the approach's robustness and improved accuracy.

The strategy complements existing regional treatment effect assessments.

Abstract

Multi-Regional Clinical Trials (MRCTs) play a central role in the development of new therapies by enabling the simultaneous evaluation of drug efficacy and safety across diverse global populations. Assessing the consistency of treatment effects across regions is a fundamental aspect of MRCTs. Existing methods typically focus on region-specific marginal treatment effects. However, when treatment effect heterogeneity arises due to effect-modifying baseline covariates, distributional differences in these covariates can lead to erroneous conclusions. In this paper, we explicitly account for this phenomenon in the consistency assessment by considering the conditional average treatment effect. We propose a two-step assessment strategy that complements existing methods and mitigates the impact of treatment effect heterogeneity. Results from numerical studies demonstrate the effectiveness of the proposed approach.