随机效应模型下 MRCT 的一致性评估与区域样本量计算
Consistency assessment and regional sample size calculation for MRCTs under random effects model
AI总结：
本文基于随机效应模型和经验收缩估计量，提出了多区域临床试验中总体与区域样本量的设计方法，并利用MHLW (2007) Method 1评估区域一致性概率，模拟与真实试验验证了方法的有效性。
摘要：
多区域临床试验（MRCTs）已成为药物研发和全球注册的常规做法。在确立总体显著性后，证明区域一致性对于地方卫生当局至关重要。已有基于固定效应模型并使用各种标准来评估此类一致性和计算区域样本量的方法。为了更好地考虑不同区域间治疗效应的异质性，随机效应模型自然成为用于设计和推断的更有效替代方案。在本文中，我们提出了总体样本量以及区域样本比例的设计。我们还基于经验收缩估计量，提供了使用 MHLW (2007) 的 Method 1 评估一致性概率的理论基础。后者随后被用于确定所需的区域样本量。我们详细阐述了其在常见的连续型、二分类和生存终点上的应用。模拟研究表明，所提出的方法将一致性概率保持在期望水平。我们使用一项糖尿病心血管结局的真实试验说明了其应用。提供了一个 R 包用于实现。
英文摘要：
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.
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