Transportability analysis is a causal inference framework used to evaluate the external validity of randomized clinical trials (RCTs) or observational studies. Most existing transportability analysis methods require individual patient-level data (IPD) for both the source and the target population, narrowing its applicability when only target aggregate-level data (AgD) is available. Besides, accounting for censoring is essential to reduce bias in longitudinal data, yet AgD-based transportability methods in the presence of censoring remain underexplored. Here, we propose a two-stage weighting framework named "Target Aggregate Data Adjustment" (TADA) to address the mentioned challenges simultaneously. TADA is designed as a two-stage weighting scheme to simultaneously adjust for both censoring bias and distributional imbalances of effect modifiers (EM), where the final weights are the product of the inverse probability of censoring weights and participation weights derived using the method of moments. We have conducted an extensive simulation study to evaluate TADA's performance. Our results indicate that TADA can effectively control the bias resulting from censoring within a non-extreme range suitable for most practical scenarios, and enhance the application and clinical interpretability of transportability analyses in settings with limited data availability.

翻译：可迁移性分析是一种用于评估随机临床试验（RCT）或观察性研究外部有效性的因果推断框架。现有的大多数可迁移性分析方法要求同时获得源总体和目标总体的个体患者水平数据（IPD），这在仅能获取目标总体汇总水平数据（AgD）时限制了其适用性。此外，在纵向数据分析中，考虑删失对于减少偏倚至关重要，然而基于AgD且能处理删失的可迁移性方法仍未被充分探索。本文提出一个名为“目标总体汇总数据调整”（TADA）的两阶段加权框架，以同时应对上述挑战。TADA设计为一个两阶段加权方案，旨在同时调整删失偏倚和效应修饰因子（EM）的分布不平衡，其最终权重是逆概率删失权重与通过矩估计法导出的参与权重的乘积。我们进行了广泛的模拟研究以评估TADA的性能。结果表明，TADA能有效控制在非极端范围内（适用于大多数实际场景）由删失引起的偏倚，并提升了数据可用性受限场景下可迁移性分析的应用价值与临床可解释性。