Quantitative bias analysis for unmeasured confounding in unanchored population-adjusted indirect comparisons.

ABSTRACT: Unanchored population-adjusted indirect comparisons (PAICs) such as matching-adjusted indirect comparison (MAIC) and simulated treatment comparison (STC) attracted a significant attention in the health technology assessment field in recent years. These methods allow for indirect comparisons by balancing different patient characteristics in single-arm studies in the case where individual patient-level data are only available for one study. However, the validity of findings from unanchored MAIC/STC analyses is frequently questioned by decision makers, due to the assumption that all potential prognostic factors and effect modifiers are accounted for. Addressing this critical concern, we introduce a sensitivity analysis algorithm for unanchored PAICs by extending quantitative bias analysis tec

SUBMITTER: Ren S 

PROVIDER: S-EPMC12527536 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature