Consistency assessment and regional sample size calculation for MRCTs under random effects model

TL;DR

This paper proposes a new method for assessing regional consistency and calculating sample sizes in multi-regional clinical trials under a random effects model, improving accuracy over fixed effects approaches.

Contribution

It introduces a novel approach for regional sample size calculation and consistency assessment using a random effects model, with theoretical validation and practical implementation.

Findings

Method retains desired consistency probability in simulations

Applicable to continuous, binary, and survival endpoints

Provides an R package for practical use

Abstract

Multi-regional clinical trials (MRCTs) have become common practice for drug development and global registration. Once overall significance is established, demonstrating regional consistency is critical for local health authorities. Methods for evaluating such consistency and calculating regional sample sizes have been proposed based on the fixed effects model using various criteria. To better account for the heterogeneity of treatment effects across regions, the random effects model naturally arises as a more effective alternative for both design and inference. In this paper, we present the design of the overall sample size along with regional sample fractions. We also provide the theoretical footage for assessing consistency probability using Method 1 of MHLW (2007), based on the empirical shrinkage estimator. The latter is then used to determine the regional sample size of interest. We elaborate on the applications to common continuous, binary, and survival endpoints in detail. Simulation studies show that the proposed method retains the consistency probability at the desired level. We illustrate the application using a real cardiovascular outcome trial in diabetes. An R package is provided for implementation.