Objectives: This study aimed to perform a simulation study to quantify the health inequality impact of a cancer therapy given cancer and treatment characteristics using the distributional cost-effectiveness framework. Methods: The following factors were varied in 10 000 simulations: lifetime risk of the disease, median overall survival (OS) with standard of care (SOC), difference in OS between non-Hispanic (NH)-Black and NH-White patients (prognostic effect), treatment effect of the new therapy relative to SOC, whether the treatment effect differs between NH-Black and NH-White patients (effect modification), health utility, drug costs, and preprogression and postprogression costs. Based on these characteristics, the incremental population net health benefits were calculated for the new therapy and applied to a US distribution of quality-adjusted life expectancy at birth. The health inequality impact was quantified as the difference in the degree of inequality in the "post-new therapy" versus "pre-new therapy" quality-adjusted life expectancy distributions. Results: For cancer types characterized by relatively large lifetime risk, large median OS with SOC, large treatment effect, and large effect modification, the direction of the impact of the new therapy on inequality is easy to predict. When effect modification is minor or absent, which is a realistic scenario, the direction of the inequality impact is difficult to predict. Larger incremental drug costs have a worsening effect on health inequality. Conclusions: The findings provide a guide to help decision makers and other stakeholders make an initial assessment whether a new therapy with known treatment effects for a specific tumor type can have a positive or negative health inequality impact.
