Multi-regional clinical trials (MRCTs) are central to global drug development, enabling evaluation of treatment effects across diverse populations. A key challenge is valid and efficient inference for a region-specific estimand when the target region is small and differs from auxiliary regions in baseline covariates or unmeasured factors. We adopt an estimand-based framework and focus on the region-specific average treatment effect (RSATE) in a prespecified target region, which is directly relevant to local regulatory decision-making. Cross-region differences can induce covariate shift, covariate mismatch, and outcome drift, potentially biasing information borrowing and invalidating RSATE inference. To address these issues, we develop a unified causal inference framework with selective information borrowing. First, we introduce an inverse-variance weighting estimator that combines a "small-sample, rich-covariate" target-only estimator with a "large-sample, limited-covariate" full-borrowing doubly robust estimator, maximizing efficiency under no outcome drift. Second, to accommodate outcome drift, we apply conformal prediction to assess patient-level comparability and adaptively select auxiliary-region patients for borrowing. Third, to ensure rigorous finite-sample inference, we employ a conditional randomization test with exact, model-free, selection-aware type I error control. Simulation studies show the proposed estimator improves efficiency, yielding 10-50% reductions in mean squared error and higher power relative to no-borrowing and full-borrowing approaches, while maintaining valid inference across diverse scenarios. An application to the POWER trial further demonstrates improved precision for RSATE estimation.

翻译：多区域临床试验（MRCTs）是全球药物开发的核心，能够评估不同人群的治疗效果。当目标区域样本量小且与辅助区域在基线协变量或未测量因素上存在差异时，如何对区域特异性估计目标进行有效且高效的推断是一个关键挑战。我们采用基于估计目标的框架，重点关注预先指定的目标区域内的区域特异性平均处理效应（RSATE），该效应与当地监管决策直接相关。跨区域差异可能导致协变量偏移、协变量失配和结局漂移，从而可能使信息借用产生偏差并使RSATE推断失效。为解决这些问题，我们开发了一个具有选择性信息借用的统一因果推断框架。首先，我们引入一种逆方差加权估计量，它将一个“小样本、丰富协变量”的仅目标区域估计量与一个“大样本、有限协变量”的全借用双稳健估计量相结合，在无结局漂移的情况下最大化效率。其次，为适应结局漂移，我们应用保形预测来评估患者层面的可比性，并自适应地选择辅助区域患者进行借用。第三，为确保严格的有限样本推断，我们采用条件随机化检验，该检验具有精确的、无模型的、选择感知的I类错误控制。模拟研究表明，所提出的估计量提高了效率，与无借用和全借用方法相比，均方误差降低了10-50%，并具有更高的统计功效，同时在不同场景下保持推断的有效性。对POWER试验的应用进一步证明了RSATE估计精度的提升。