In observational studies, covariate imbalance generates confounding, resulting in biased comparisons. Although propensity score-based weighting approaches facilitate unconfounded group comparisons for implicit target populations, existing techniques may not directly or efficiently analyze multiple studies with multiple groups and provide results generalizable to larger populations. Moreover, few methods deliver precise inferences for various estimands with censored survival outcomes. We propose a new concordant target population approach, which constructs generalized balancing weights and realistic target populations. Our method can incorporate researcher-specified natural population attributes and synthesize information by appropriately compensating for over- or under-represented groups to achieve covariate balance. The constructed {concordant} weights are agnostic to specific estimators, estimands, and outcomes and maximize the effective sample size (ESS) for more precise inferences. Simulation studies and descriptive comparisons of glioblastoma outcomes of racial groups in multiple TCGA studies demonstrate the strategy's practical advantages. Unlike existing weighting techniques, the proposed concordant target population revealed a drastically different result: Blacks were more vulnerable and endured significantly worse prognoses; Asians had the best outcomes with a median overall survival of 1,024 (SE: 15.2) days, compared to 384 (SE: 1.2) and 329 (SE: 19.7) days for Whites and Blacks, respectively.

翻译：在观测研究中，协变量失衡会导致混杂偏倚，进而造成有偏的比较。尽管基于倾向性评分的加权方法能够为隐含目标人群提供无混杂的组间比较，但现有技术可能无法直接或高效地分析包含多组别的多项研究，并提供可推广至更大人群的结果。此外，针对删失生存结局的多种估计量，很少有方法能提供精确推断。我们提出了一种新的协和目标人群方法，该方法构建了广义平衡权重和现实目标人群。我们的方法能够纳入研究者指定的自然人群属性，并通过适当补偿过度或不足代表性的组别来整合信息，以实现协变量平衡。所构建的协和权重对特定估计量、估计目标及结局具有无关性，并能最大化有效样本量以支持更精确的推断。模拟研究以及对多项TCGA研究中胶质母细胞瘤种族结局的描述性比较，展示了该策略的实际优势。与现有加权技术不同，所提出的协和目标人群揭示了截然不同的结果：黑人群体更易感且预后显著更差；亚洲人预后最佳，总生存期中位数为1024天（标准误：15.2），而白人和黑人分别为384天（标准误：1.2）和329天（标准误：19.7）。