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

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 techniques traditionally used in epidemiology. Our proposed sensitivity analysis involves simulating important covariates that were not reported by the comparator study when conducting unanchored STC and enables the formal evaluating of the impact of unmeasured confounding in a quantitative manner without additional assumptions. We demonstrate the practical application of this method through a real-world case study of metastatic colorectal cancer, highlighting its utility in enhancing the robustness and credibility of unanchored PAIC results. Our findings emphasise the necessity of formal quantitative sensitivity analysis in interpreting unanchored PAIC results, as it quantifies the robustness of conclusions regarding potential unmeasured confounders and supports more robust, reliable, and informative decision-making in healthcare.