Expanding Cost-Effectiveness Analysis to Demonstrate Population-Level Treatment Impact on Patients and Society

OBJECTIVES: In this framework, we discuss the similarities and differences between the quality-adjusted life years (QALY) metric and its alternatives – equal-value life years (evLY) and healthy years in total (HYT) – and introduce population-level models as a creative way to leverage cost-effectiveness analyses (CEA) outcomes beyond QALYs and incremental cost-effectiveness ratios (ICERs) to demonstrate the value of treatments and the impact on patients and society.

METHODS: Scenarios with different gains and losses in health utility and life years were used to estimate QALY, evLY, HYT, and corresponding ICERs to compare hypothetical treatments A and B. We investigated whether the metric choice affects the cost-effectiveness vs. standard of care (SOC) using different willingness-to-pay (WTP) thresholds ($100k for QALY [Vanness et al. 2021], $84k for evLY [Campbell et al. 2023], $72k for HYT [Basu et al. 2020]). For specific disease areas, population models were considered to highlight alternative health and cost outcomes – beyond ICERs, QALYs, and QALY alternatives – to demonstrate differences in treatment value and impact on patients and society.

RESULTS: The results show that despite differences in the metric values (QALY ≤ evLY ≤ HYT), the cost-effectiveness outcome is rarely affected given that the appropriate WTP is used. While each of these metrics captures treatment impacts on patients’ quality and quantity of life, they each have important strengths and limitations. Additional health and economic outcomes can be generated from CEA and scaled up to the population level. These outcomes may include disease progressions and deaths avoided, cost offsets for subsequent treatments, and productivity losses.

CONCLUSIONS: QALY alternatives, including evLY and HYT, and population models can be used to complement the more conventional CEA approach anchoring to QALYs and ICERs. This expansion of CEA methods allows further understanding of differences between treatments and their impacts on patients and society for key population-based decision-makers.