Multi-regional clinical trials (MRCTs) play an increasingly crucial role in global pharmaceutical development by expediting data gathering and regulatory approval across diverse patient populations. However, differences in recruitment practices and regional demographics often lead to variations in study participant characteristics, potentially biasing treatment effect estimates and undermining treatment effect consistency assessment across regions. To address this challenge, we propose novel estimators and inference methods utilizing inverse probability of sampling and calibration weighting. Our approaches aim to eliminate exogenous regional imbalance while preserving intrinsic differences across regions, such as race and genetic variants. Moreover, time-to-event outcomes in MRCT studies receive limited attention, with existing methodologies primarily focusing on hazard ratios. In this paper, we adopt restricted mean survival time to characterize the treatment effect, offering more straightforward interpretations of treatment effects with fewer assumptions than hazard ratios. Theoretical results are established for the proposed estimators, supported by extensive simulation studies. We illustrate the effectiveness of our methods through a real MRCT case study on acute coronary syndromes.

翻译：多区域临床试验在全球药物开发中发挥着日益关键的作用，通过加速跨不同患者群体的数据收集和监管审批。然而，招募实践和区域人口统计学上的差异常导致研究参与者特征存在差异，可能使治疗效果估计产生偏倚，并破坏跨区域疗效一致性的评估。为解决这一挑战，我们提出了利用逆抽样概率和校准加权的新颖估计量及推断方法。我们的方法旨在消除外源性区域不平衡，同时保留区域固有的内在差异（如种族和遗传变异）。此外，多区域临床试验中事件发生时间结局的研究关注不足，现有方法主要集中于风险比。本文采用有限均值生存时间来表征治疗效果，与风险比相比，该指标能在假设更少的条件下提供更直观的治疗效果解释。我们为所提出的估计量建立了理论结果，并通过广泛模拟研究加以验证。通过一项关于急性冠脉综合征的真实多区域临床试验案例，我们展示了方法的有效性。