OBJECTIVES

As specialized immunotherapies become more complicated and costly, the increased need for evaluation and prioritization arises from limited R&D funds and healthcare provider budgets. T-cell therapy for Acute Myeloid Leukemia (AML) is a personalized therapy as it takes blood cells from healthy donors, expands T-lymphocytes for 2 to 4 weeks following exposure to immunogenic peptides present on the surface of malignant cells and then infuses them back into the patient. Our study aims to investigate the cost-effectiveness of immunotherapy compared to chemotherapy.

METHODS

We constructed a stochastic Markov model, to calculate the costs and outcomes for a hypothetical cohort of patients with AML who had relapsed after allo-HSCT; assuming they were treated with either chemotherapy or T-cell immunotherapy. The incremental cost effectiveness ratios (ICERs) were calculated using average costs and QALYs. Two scenarios in our cost-effectiveness analysis were considered: Base cost-effectiveness analysis and threshold analysis. We performed a value-of-information analysis to identify priority areas for future research under various scenarios.

RESULTS

The base cost-effectiveness analysis scenario has shown that immunotherapy is more effective but more costly than chemotherapy. Based on the threshold analysis, the required threshold and hazard ratio for immunotherapy to become cost-effective was $100K per QALY and HR_PD=0.098 respectively. The cost-effectiveness was assessed based on ICER which was $99,656.74 per QALY in this case. Based on the value of information analysis, research to obtain perfect information becomes less valuable at threshold values higher than ICER.

CONCLUSIONS

The base cost-effectiveness analysis scenario has shown that immunotherapy is not cost-effective for a willingness-to-pay threshold of $50K per QALY. Based on a threshold cost-effectiveness analysis immunotherapy becomes cost-effective at a threshold of $100K per QALY. Our study provides a structured evaluation of the new technologies at early stages of commercialization and can guide the design of future R&D efforts.