A New Integrative Method for Multigroup Comparisons of Censored Survival Outcomes in Multiple Observational Studies

In observational studies, covariate imbalance generates confounding, resulting in biased outcome 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, provide generalizable results for the larger population, or deliver precise inferences for various estimands with censored survival outcomes. We construct generalized balancing weights and realistic target populations that incorporate researcher-specified natural population attributes and synthesize information by appropriately compensating for over- or under-represented groups to achieve covariate balance. The concordant weights are agnostic to specific estimators, estimands, and outcomes because they maximize the effective sample size (ESS) to deliver precise inferences. To identify the concordant population, theoretical results identify the global maximum of ESS for a conditional target density. 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 OS of 1,024 (SE: 15.2) days, compared to 384 (SE: 1.2) and 329 (SE: 19.7) days for Whites and Blacks, respectively.