Economic evaluation of margetuximab vs. trastuzumab for pretreated ERBB2-positive advanced breast cancer in the US and China.

Objective: To assess the economic evaluation of margetuximab plus chemotherapy over trastuzumab plus chemotherapy for women with pretreated ERBB2-positive advanced breast cancer in the United States (US) and China.
Methods: Based on the SOPHIA trial, a three-state Markov model was developed to compare the cost and efficacy of margetuximab to trastuzumab for previously treated women with ERBB2-positive advanced breast cancer. The model inputs were derived from existing literature and the US life table. Primary outcomes included lifetime costs in US dollars, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratio (ICER). Deterministic and probabilistic sensitivity analyses were conducted to evaluate the impact of uncertainty.
Results: The base case analyses demonstrated that margetuximab plus chemotherapy had an increasing cost of $68,132 and $20,540 over trastuzumab plus chemotherapy in the US and China, respectively, with a gain of 0.11 and 0.09 QALYs both favored margetuximab. The ICERs for two treatment strategies were $260,176 in the US and $630,777 in China, resulting in a poor cost-effectiveness at their respective threshold of willingness to play. One-way sensitivity analyses showed that the results to be most sensitive to the price of margetuximab and that of trastuzumab. And an 11 and 82% price reduction of margetuximab would make this regimen cost-effective in the US and China, respectively.
Conclusion: In the US and China, margetuximab plus chemotherapy is not likely to be cost-effective for women with pretreated ERBB2-positive advanced breast cancer, whereas price reduction effectively improves insufficient cost-effectiveness.
Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
(Copyright © 2022 Tang, Xu, Gao, Chen, Zhu, Wang, Yan, Chen and Zhu.)