OBJECTIVES: To perform a simulation study to investigate the health equity impact of a hypothetical new cancer therapy in comparison to standard of care (SOC) as a function of treatment and disease characteristics.

METHODS: Simulations were performed with a 3-state partitioned survival model based on progression free survival (PFS) and overall survival (OS) curves that varied by treatment and subgroup of the target patient population. Given health state-specific utility and cost values, expected net health benefit (NHB = QALYs – costs/opportunity cost threshold) was calculated for both treatments by subgroup assuming equally distributed opportunity costs. The health equity impact from a change in inequality in the NHBs across subgroups with the new treatment relative to SOC was expressed with Atkinson and Kolm inequality metrics. Importance of the following factors was evaluated in 5000 simulations covering a range of realistic parameter values: PFS and OS with SOC and degree of heterogeneity in these outcomes, relative treatment effect of the new intervention and degree of effect modification, utility, drug cost, and other medical cost. Simulations were performed with the R hesim package.

RESULTS: Simulations showed that a new cancer treatment’s impact on inequality in health outcomes was reduced (increased) when its efficacy was better (worse) among the sub-populations benefitting the least from SOC. The largest effect on health equity impact metrics was observed when the relative treatment effects with the new intervention and PFS/OS hazard rates with SOC varied at least 10% between two subgroups. The impact of these two factors was amplified when a cancer had longer PFS and OS, the difference in treatment costs increased, inequality aversion was larger, or the opportunity cost threshold was lower.

CONCLUSIONS: Findings from this study provide insight into conditions that determine whether a novel cancer therapy can have a meaningful impact on inequality in outcomes.