  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.
