Cost-effectiveness analysis of sugemalimab vs. placebo, in combination with chemotherapy, for treatment of first-line metastatic NSCLC in China.

Objective: The purpose of this study was to estimate the cost-effectiveness of sugemalimab plus chemotherapy (SC) vs. placebo plus chemotherapy (PC), as the first-line treatment for patients with non-small cell lung cancer (NSCLC) in China.
Material and Methods: A three-state Markov model with a cycle of 3 weeks was built to assess the incremental cost-effectiveness ratio (ICER) of SC vs. PC as first-line treatment for patients with NSCLC over a 10-year horizon from Chinese health care perspective. Time-dependency transition probability and safety data were derived from a multicenter, randomized, double-blind, phase 3 clinical trial performed in China (GEMSTONE-302). Primary model outcomes included the costs in US dollars and health outcomes in quality-adjusted life-years (QALYs) and the ICER under a willingness-to-pay (WTP) threshold of $37,663/QALYs. Deterministic, scenario and probabilistic sensitivity analysis were employed to investigate the robustness of model outcomes.
Results: In base-case analysis, compared with PC, first-line SC for intention-to-treat (ITT) population gained an additional 0.57 QALYs with an incremental cost of $62,404.15, resulting in an ICER of $109,480.97/QALYs gained. When a patient assistance program (PAP) was available, the ICER decreased to $52,327.02/QALYs. In subgroup analysis, the ICER values were above the WTP threshold with or without PAP. Sensitivity analysis results suggested that the model outcomes were reliable.
Conclusion: From the perspective of Chinese healthcare system, the SC was not cost-effective in comparison to PC as first-line treatment for NSCLC, regardless of PD-L1 tumor expression level and pathological subtype.
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 Li and Wan.)