A New Integrative Method for Multigroup Comparisons of Censored Survival Outcomes in Multiple Observational Studies
Subharup Guha, David C. Christiani, and Yi Li

TL;DR
This paper introduces a novel weighting method for multigroup comparisons of censored survival data across multiple observational studies, improving covariate balance and inference precision for larger, more representative populations.
Contribution
It proposes a new concordant target population approach that constructs generalized balancing weights, enhancing covariate balance and inference accuracy in multigroup survival analyses.
Findings
Demonstrated practical advantages through simulations and glioblastoma data analysis.
Revealed significant survival differences among racial groups, with Blacks worse off and Asians best.
Produced more accurate and generalizable survival comparisons across studies.
Abstract
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…
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Taxonomy
TopicsAdvanced Causal Inference Techniques · Statistical Methods in Clinical Trials · Statistical Methods and Bayesian Inference
